text:
#### Idea
Reverse Classification Accuracy (RCA) models are aims to answer the question on how to estimate performance of models (semantic segmentation models were explained in the paper) in cases where ground truth is not available.
#### Why is it important
Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. RCA aims to quantify the performance in those deployment scenarios
#### Methodology
The RCA model pipeline follows a simple enough pipeline for the same:
1. Train a model M on training dataset T containing input images and ground truth {**I**,**G**}
2. Use M to predict segmentation map for an input image II to get segmentation map SS
3. Train a RCA model that uses input image II to predict SS. As it's a single datapoint for the model it would overfit. There's no validation set for the RCA model
4. Test the performance of RCA model on Images which have ground truth G and the best performance of the model is an indicator of the performance (DSC - Dice Similarity Coefficient) of how the original image would perform on a new image whose ground truth is not available to compute segmentation accuracy (DSC)
#### Observation
For validation of the RCA method, the predicted DSC and the real DSC were compared and the correlation between the 2 was calculated. For all calculations 3 types of methods of segmentation were used and 3 slightly different types methods for RCA were used for comparison. The predicted DSC and real DSC were highly correlated for most of the cases.
Here's a snap of the results that they obtained
Generates abstractive summaries from news articles. Also see [blog](https://metamind.io/research/your-tldr-by-an-ai-a-deep-reinforced-model-for-abstractive-summarization)
* Input:
* vocab size 150K
* start with $W_\text{emb}$ Glove 100
* Seq2Seq:
* bidirectional LSTM, `size=200` in each direction. Final hidden states are concatenated and feed as initial hidden state of the decoder an LSTM of `size=400`. surprising it's only one layer.
* Attention:
* Add standard attention mechanism between each new hidden state of the decoder and all the hidden states of the encoder
* A new kind of attention mechanism is done between the new hidden state of the decoder and all previous hidden states of the decoder
* the new hidden state is concatenated with the two attention outputs and feed to dense+softmax to model next word in summary (output vocab size 50K). The weight matrix $W_h$ is reduced to $W_h = \tanh \left( W_\text{emb} W_\text{proj} \right) $ resulting in faster converges, see [1](arXiv:1611.01462) and [2](https://arxiv.org/abs/1608.05859)
* Pointer mechanism:
* The concatenated values are also feed to logistic classifier to decide if the softmax output should be used or one of the words in the article should be copied to the output. The article word to be copied is selected using same weights computed in the attention mechanism
* Loss
* $L_\text{ml}$: NLL of the example summary $y^*$. If only $L_\text{ml}$ is used then 25% of the times use generated instead of given sample as input to next step.
* $L_\text{rl}$: sample an entire summary from the model $y^s$ (temperature=1) and the loss is the NLL of the sample multiplied by a reward. The reward is $r(y^s)-r(\hat{y})$ where $r$ is ROUGE-L and $\hat{y}$ is a generated greedy sequences
* $L=\gamma L_\text{rl} + (1-\gamma)L_\text{ml}$ where $\gamma=0.9984$
* Training
* `batch=50`, Adam, `LR=1e-4` for RL/ML+RL training
* The training labels are summary examples and an indication if copy was used in the pointer mechanism and which word was copied. This is indicated when the summary word is OOV or if it appears in the article and its NER is one of PERSON, LOCATION, ORGANIZATION or MISC
* Generation
* 5 beams
* force trigrams not to appear twice in the same beam
This very new paper, is currently receiving quite a bit of attention by the [community](https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/).
The paper describes a new training approach, which solves the two major practical problems with current GAN training:
1) The training process comes with a meaningful loss. This can be used as a (soft) performance metric and will help debugging, tune parameters and so on.
2) The training process does not suffer from all the instability problems. In particular the paper reduces mode collapse significantly.
On top of that, the paper comes with quite a bit mathematical theory, explaining why there approach works and other approachs have failed. This paper is a must read for anyone interested in GANs.
Representation (or feature) learning with unsupervised learning has yet really to yield the type of results that many believe to be achievable. For example, we'd like to unleash an unsupervised learning algorithm on all web images and then obtain a representation that captures the various factors of variation we know to be present (e.g. objects and people). One popular approach for this is to train a model that assumes a high-level vector representation with independent components. However, despite a large body of literature on such models by now, such so-called disentangling of these factors of variation still seems beyond our reach.
In this short paper, the authors propose an alternative to this approach. They propose that disentangling might be achievable by learning a representation whose dimensions are each separately **controllable**, i.e. that each have an associated policy which changes the value of that dimension **while letting other dimensions fixed**.
Specifically, the authors propose to minimize the following objective:
$\mathop{\mathbb{E}}_s\left[\frac{1}{2}||s-g(f(s))||^2_2 \right] - \lambda \sum_k \mathbb{E}_{a,s}\left[\sum_a \pi_k(a|s) \log sel(s,a,k)\right]$
- $s$ is an agent's state (e.g. frame image) which encoder $f$ and decoder $g$ learn to autoencode
- $k$ iterates over all dimensions of the representation space (output of encoder)
- $a$ iterates over actions that the agent can take
- $\pi_k(a|s)$ is the policy that is meant to control the $k^{\rm th}$ dimension of the representation space $f(s)_k$
- $sel(s,a,k)$ is the selectivity of $f(s)_k$ relative to other dimensions in the representation, at state $s$:
$sel(s,a,k) = \mathop{\mathbb{E}}_{s'\sim {\cal P}_{ss'}^a}\left[\frac{|f_k(s')-f_k(s)|}{\sum_{k'} |f_{k'}(s')-f_{k'}(s)| }\right]$
${\cal P}_{ss'}^a$ is the conditional distribution over the next step state $s'$ given that you are at state $s$ and are taking action $a$ (i.e. the environment transition distribution). One can see that selectivity is higher when the change $|f_k(s')-f_k(s)|$ in dimension $k$ is much larger than the change
$|f_{k'}(s')-f_{k'}(s)|$ in the other dimensions $k'$. A directed version of selectivity is also proposed (and I believe was used in the experiments), where the absolute value function is removed and $\log sel$ is replaced with $\log(1+sel)$ in the objective.
The learning objective will thus encourage the discovery of a representation that is informative of the input (in that you can reconstruct it) and for which there exists policies that separately control these dimensions.
Algorithm 1 in the paper describes a learning procedure for optimizing this objective. In brief, for every update, a state $s$ is sampled from which an update for the autoencoder part of the loss can be made. Then, iterating over each dimension $k$, REINFORCE is used to get a gradient estimate of the selectivity part of the loss, to update both the policy $\pi_k$ and the encoder $f$ by using the policy to reach a next state $s'$.
**My two cents**
I find this concept very appealing and thought provoking. Intuitively, I find the idea that valuable features are features which reflect an aspect of our environment that we can control more sensible and possibly less constraining than an assumption of independent features. It also has an interesting analogy of an infant learning about the world by interacting with it.
The caveat is that unfortunately, this concept is currently fairly impractical, since it requires an interactive environment where an agent can perform actions, something we can't easily have short of deploying a robot with sensors. Moreover, the proposed algorithm seems to assume that each state $s$ is sampled independently for each update, whereas a robot would observe a dependent stream of states.
Accordingly, the experiments in this short paper are mostly "proof of concept", on simplistic synthetic environments. Yet they do a good job at illustrating the idea.
To me this means that there's more interesting work worth doing in what seems to be a promising direction!
Mask RCNN takes off from where Faster RCNN left, with some augmentations aimed at bettering instance segmentation (which was out of scope for FRCNN). Instance segmentation was achieved remarkably well in *DeepMask* , *SharpMask* and later *Feature Pyramid Networks* (FPN).
Faster RCNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is most evident in how RoIPool , the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. Mask RCNN fixes that by introducing RoIAlign in place of RoIPool.
#### Methodology
Mask RCNN retains most of the architecture of Faster RCNN. It adds the a third branch for segmentation. The third branch takes the output from RoIAlign layer and predicts binary class masks for each class.
##### Major Changes and intutions
**Mask prediction**
Mask prediction segmentation predicts a binary mask for each RoI using fully convolution - and the stark difference being usage of *sigmoid* activation for predicting final mask instead of *softmax*, implies masks don't compete with each other. This *decouples* segmentation from classification. The class prediction branch is used for class prediction and for calculating loss, the mask of predicted loss is used calculating Lmask.
Also, they show that a single class agnostic mask prediction works almost as effective as separate mask for each class, thereby supporting their method of decoupling classification from segmentation
RoIPool first quantizes a floating-number RoI to the discrete granularity of the feature map, this quantized RoI is then subdivided into spatial bins which are themselves quantized, and finally feature values covered by each bin are aggregated (usually by max pooling). Instead of quantization of the RoI boundaries
or bin bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and aggregate the result (using max or average).
**Backbone architecture**
Faster RCNN uses a VGG like structure for extracting features from image, weights of which were shared among RPN and region detection layers. Herein, authors experiment with 2 backbone architectures - ResNet based VGG like in FRCNN and ResNet based [FPN](http://www.shortscience.org/paper?bibtexKey=journals/corr/LinDGHHB16) based. FPN uses convolution feature maps from previous layers and recombining them to produce pyramid of feature maps to be used for prediction instead of single-scale feature layer (final output of conv layer before connecting to fc layers was used in Faster RCNN)
**Training Objective**
The training objective looks like this
Lmask is the addition from Faster RCNN. The method to calculate was mentioned above
#### Observation
Mask RCNN performs significantly better than COCO instance segmentation winners *without any bells and whiskers*. Detailed results are available in the paper
The authors introduce their contribution as an alternative way to approximate the KL divergence between prior and variational posterior used in [Variational Dropout and the Local Reparameterization Trick][kingma] which allows unbounded variance on the multiplicative noise. When the noise variance parameter associated with a weight tends to infinity you can say that the weight is effectively being removed, and in their implementation this is what they do.
There are some important details differing from the [original algorithm][kingma] on per-weight variational dropout. For both methods we have the following initialization for each dense layer:
theta = initialize weight matrix with shape (number of input units, number of hidden units)
log_alpha = initialize zero matrix with shape (number of input units, number of hidden units)
b = biases initialized to zero with length the number of hidden units
Where `log_alpha` is going to parameterise the variational posterior variance.
In the original paper the algorithm was the following:
mean = dot(input, theta) + b # standard dense layer
# marginal variance over activations (eq. 10 in [original paper][kingma])
variance = dot(input^2, theta^2 * exp(log_alpha))
# sample from marginal distribution by scaling Normal
activations = mean + sqrt(variance)*unit_normal(number of output units)
The final step is a standard [reparameterization trick][shakir], but since it is a marginal distribution this is referred to as a local reparameterization trick (directly inspired by the [fast dropout paper][fast]).
The sparsifying algorithm starts with an alternative parameterisation for `log_alpha`
log_sigma2 = matrix filled with negative constant (default -8) with size (number of input units, number of hidden units)
log_alpha = log_sigma2 - log(theta^2)
log_alpha = log_alpha clipped between 8 and -8
The authors discuss this in section 4.1, the $\sigma_{ij}^2$ term corresponds to an additive noise variance on each weight with $\sigma_{ij}^2 = \alpha_{ij}\theta_{ij}^2$. Since this can then be reversed to define `log_alpha` the forward pass remains unchanged, but the variance of the gradient is reduced. It is quite a counter-intuitive trick, so much so I can't quite believe it works.
They then define a mask removing contributions to units where the noise variance has gone too high:
clip_mask = matrix shape of log_alpha, equals 1 if log_alpha is greater than thresh (default 3)
The clip mask is used to set elements of `theta` to zero, and then the forward pass is exactly the same as in the original paper.
The difference in the approximation to the KL divergence is illustrated in figure 1 of the paper; the sparsifying version tends to zero as the variance increases, which matches the true KL divergence. In the [original paper][kingma] the KL divergence would explode, forcing them to clip the variances at a certain point.
[kingma]: https://arxiv.org/abs/1506.02557
[shakir]: http://blog.shakirm.com/2015/10/machine-learning-trick-of-the-day-4-reparameterisation-tricks/
[fast]: http://proceedings.mlr.press/v28/wang13a.html
## Problems
* **Computer Vision**:
* Detection: Given a 2D image, where are cars, pedestrians, traffic signs?
* Depth estimation: Given a 2D image, estimate the depth
* **Planning**: Where do I want to go?
* **Control**: How should I steer?
## Datasets
* KITTI: Street segmentation (Computer Vision)
* Cityscapes
## What I missed
* GTSRB: The German Traffic Sign Recognition Benchmark dataset
* GTSDB: The German Traffic Sign Detection Benchmark
The paper proposes a reusable neural network module to `reason about the relations between entities and their properties`:
$$ RN(O) = f_\phi \left( \sum_{i,j} g_\theta(o_i, o_j) \right), $$
- $O$ is a set of input objects $\{o_1, o_2, ..., o_n\}, o_i \in R^m$
- $g_\theta$ is a neural network (MLP) which approximates object-to-object relation function
- $f_\phi $ is a neural network (MLP) which transforms summed pairwise object-to-object relations to some desired output
RN's operate on sets (due to summation in the formula) and thus are invariant to the order of objects in the input.
In terms of architecture, RN module is used at the tail of a neural network taking input objects in form of CNN or LSTM embeddings.
This work is evaluated on several tasks where it achieves reasonably good (even superhuman) performance:
- CLEVR and Sort-of-CLEVR - question answering about an image
- bAbI - text based question answering
- Dynamic physical system - MuJoCo simulations with physical relation between entities
This paper described an algorithm of parametrically adding noise and applying a variational regulariser similar to that in ["Variational Dropout Sparsifies Deep Neural Networks"][vardrop]. Both have the same goal: make neural networks more efficient by removing parameters (and therefore the computation applied with those parameters). Although, this paper also has the goal of giving a prescription of how many bits to store each parameter with as well.
There is a very nice derivation of the hierarchical variational approximation being used here, which I won't try to replicate here. In practice, the difference to prior work is that the stochastic gradient variational method uses hierarchical samples; ie it samples from a prior, then incorporates these samples when sampling over the weights (both applied through local reparameterization tricks). It's a powerful method, which allows them to test two different priors (although they are clearly not limited to just these), and compare both against competing methods. They are comparable, and the choice of prior offers some tradeoffs in terms of sparsity versus quantization.
[vardrop]: http://www.shortscience.org/paper?bibtexKey=journals/corr/1701.05369
Video prediction with human objects
Instead of the common approach of predicting directly in pixel-space, use explicit knowledge of human motion space to predict the future of the video.
1. VAE to model the possible future movements of humans in the pose space
2. Conditional GAN - use pose information for to predict video in pixel space.
This paper is about transfer learning for computer vision tasks.
## Contributions
* Before this paper, people focused on similar datasets (e.g. ImageNet-like images) or even the same dataset but a different task (classification -> segmentation). This paper, they look at extremely different dataset (ImageNet-like vs text) but only one task (classification). They show that all layers can be shared (including the last classification layer) between datasets such as MNIST and CIFAR-10
* Normalizing information is necessary for sharing models between datasets in order to compensate for dataset-specific differences. Domain-specific scaling parameters work well.
## Evaluation
* Used datasets:
1. MNIST (10 classes: handwritten digits 0-9),
2. SVHN (10 classes: house number digits, 0-9),
3. [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (10 classes: airplane, automobile, bird, ...)
4. Daimler Mono Pedestrian Classification Benchmark (18 × 36 pixels)
5. Human Sketch dataset (20000 human sketches of every day objects such as "book", "car", "house", "sun")
6. German Traffic Sign Recognition (GTSR) Benchmark (43 traffic signs)
7. Plankton imagery data (classification benchmark that contains 30336 images of various organisms ranging from the smallest single-celled protists to copepods, larval fish, and larger jellies)
8. Animals with Attributes (AwA): 30475 images of 50 animal species (for zero-shot learning)
9. Caltech-256: object classification benchmark (256 object categories and an additional background class)
10. Omniglot: 1623 different handwritten characters from 50 different alphabets (one shot learning)
* images are resized to 64 × 64 pixels, greyscale ones are converted into RGB by setting the three channels to the same value
* Each dataset is also whitened, by subtracting its mean and dividing it by its standard deviation per channel
* **Architecture**: ResNet + Global Average Pooling + FC with Softmax
* "As the majority of the datasets have a different number of classes, we use a dataset-specific fully connected layer in our experiments unless otherwise stated."
* **Data augmentation**: We follow the same data augmentation strategy in [[18]](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeZRS15), the 64 × 64 size whitened image is padded with 8 pixels on all sides and a 64×64 patch randomly sampled from the padded image or its horizontal flip (except for MNIST / Omniglot / SVHN, as those contain text)
* **Training**: stochastic gradient descent with momentum
Sharing strategies:
1. Baseline: Train networks for each dataset independantly
2. Full sharing: For MNIST / SVHN / CIFAR-10, group classes randomly together so that Node 2 might be digit "7" for MNIST, digit "3" for SVHN and "aeroplane" for CIFAR-10. They are trained together in one network.
3. Deep sharing: Share all layers except the last one. Use all 10 datasets for this.
4. Partial sharing: Have a dataset-specific first part to compensate for different image statistics, but share the middle of the network.
The results seem to be inconclusive to me.
## Follow-up / related work
#### Very Brief Summary:
This paper combines stochastic variational inference with memory-augmented recurrent neural networks. The authors test 4 variants of their models against the Variational Recurrent Neural Network on 7 artificial tasks requiring long term memory. The reported log-likelihood lower bound is not obviously improved by the new models on all tasks but is slightly better on tasks requiring high capacity memory.
#### Slightly Less Brief Summary:
The authors propose a general class of generative models for time-series data with both deterministic and stochastic latents. The deterministic latents, $h_t$, evolve as a recurrent net with augmented memory and the stochastic latents, $z_t$ are gaussians whose mean and variance are a deterministic function of $h_t$. The observations at each time-step $x_t$ are also gaussians whose mean and variance are parametrised by a function of $h_{<t}, x_{<t}$.
#### Generative Temporal Models without Augmented Memory:
The family of generative temporal models is fairly broad and includes kalman filters, non-linear dynamical systems, hidden-markov models and switching state-space models. More recent non-linear models such as the variational RNN are most similar to the new models in this paper. In general all of the mentioned temporal models can be written as:
$P_\theta(x_{\leq T}, z_{\leq T} ) = \prod_t P_\theta(x_t | f_x(z_{\leq t}, x_{\leq t}))P_\theta(z_t | f_z(z_{\leq t}, x_{\leq t}))$
The differences between models then come from the the exact forms of $f_x$ and $f_z$ with most models making strong conditional independence assumptions and/or having linear dependence. For example in a Gaussian State Space model both $f_x$ and $f_z$ are linear, the latents form a first order Markov chain and the observations $x_t$ are conditionally independent of everything given $z_t$.
In the Variational Recurrent Neural Net (VRNN) an additional deterministic latent variable $h_t$ is introduced and at each time-step $x_t$ is the output of a VAE whose prior $z_t$ is conditioned on $h_t$. $h_t$ evolves as an RNN.
#### Types of Model with Augmented Memory:
This paper follows the same strategy as the VRNN but adds more structure to the underlying recurrent neural net. The authors motivate this by saying that the VRNN "scales poorly when higher capacity storage is required".
* "Introspective" Model: In the first augmented memory model, the deterministic latent M_t is simply a concatenation of the last $L$ latent stochastic variables $z_t$. A soft method of attention over the latent memory is used to generate a "memory context" vector at each time step. The observed output $x_t$ is a gaussian with mean and variance parameterised by the "memory context' and the stochastic latent $z_t$. Because this model does not learn to write to memory it is faster to train.
* In the later models the memory read and write operations are the same as those in the neural turing machine or differentiable neural computer.
#### My Two Cents:
In some senses this paper feels fairly inevitable since VAE's have already been married with RNNs and so it's a small leap to add augmented memory.
The actual read write operations introduced in the "introspective" model feel a little hacky and unprincipled.
The actual images generated are quite impressive.
I'd like to see how these kind of models do on language generation tasks and wether they can be adapted for question answering.
[Batch Normalization Ioffe et. al 2015](Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift) is one of the remarkable ideas in the era of deep learning that sits with the likes of Dropout and Residual Connections. Nonetheless, last few years have shown a few shortcomings of the idea, which two years later Ioffe has tried to solve through the concept that he calls Batch Renormalization.
Issues with Batch Normalization
- Different parameters used to compute normalized output during training and inference
- Using Batch Norm with small minibatches
- Non-i.i.d minibatches can have a detrimental effect on models with batchnorm. For e.g. in a metric learning scenario, for a minibatch of size 32, we may randomly select 16 labels then choose 2 examples for each of these labels, the examples interact at every layer and may cause model to overfit to the specific distribution of minibatches and suffer when used on individual examples.
The problem with using moving averages in training, is that it causes gradient optimization and normalization in opposite direction and leads to model blowing up.
Idea of Batch Renormalization
We know that,
${\frac{x_i - \mu}{\sigma} = \frac{x_i - \mu_B}{\sigma_B}.r + d}$
${r = \frac{\sigma_B}{\sigma}, d = \frac{\mu_B - \mu}{\sigma}}$
So the batch renormalization algorithm is defined as follows
![Batch Renorm Algo](https://fractalanalytic-my.sharepoint.com/personal/shubham_jain_fractalanalytics_com/_layouts/15/guestaccess.aspx?docid=0c2c627424786442f8de65367755e1fd1&authkey=ARSCi3QfpM_uBVuWCYARKNg)
Ioffe writes further that for practical purposes,
> In practice, it is beneficial to train the model for a certain number of iterations with batchnorm alone, without the correction, then ramp up the amount of allowed correction. We do this by imposing bounds on r and d, which initially constrain them to 1 and 0, respectively, and then are gradually relaxed.
In experiments,
For Batch Renorm, author used $r_{max}$ = 1, $d_{max}$ = 0 (i.e. simply batchnorm) for the first 5000 training steps, after which these were gradually relaxed to reach $r_{max}$ = 3 at 40k steps, and $d_{max}$ = 5 at 25k steps. A training step means, an update to the model.
Summary from [reddit](https://www.reddit.com/r/MachineLearning/comments/623oq4/r_early_stopping_without_a_validation_set/dfjzwqq/):
We want to minimize the expected risk (loss) but that's a mean over the real distribution of the data, which we don't know. We approximate that by using a finite dataset and try to minimize the empirical risk instead.
The gradients for the empirical risk are an approximation to the gradients for the expected risk.
The idea is that the real gradients contain just information whereas the approximated gradients contain information + noise. The noise results from using a finite dataset to approximate the real distribution of the data.
By computing local statistics about the gradients, the authors are able to determine when the gradients have no information about the expected risk anymore and what's left is just noise. If we keep optimizing we're going to overfit.
* Output can contain several sentences, that are considered as a single long sequence.
* Seq2Seq+attention:
* Oddly they use the formula used by Bahdanau attention weights to combine the weighted attention $c_t$ with the decoder output $h_t^T = W_0 \tanh \left( U_h h_t^T + W_h c_t \right) $ while the attention weights are computed with softmax over dot product between encoder and decoder outputs $h_t^T \cdot h_i^S$
* Glove 300
* 2 layer LSTM 256
* RL model
* Reward=Simplicity+Relevance+Fluency = $\lambda^s r^S + \lambda^R r^R + \lambda^F r^F$
* $r^S = \beta \text{SARI}(X,\hat{Y},Y) + (1-\beta) \text{SARI}(X,Y,\hat{Y})$
* $r^R$ cosine of output of RNN auto encoder run on input and a separate auto encoder run on output
* $r^F$ perplexity of LM trained on output
* Learning exactly as in [MIXER](https://arxiv.org/abs/1511.06732)
* Lexical Simplification model: they train a second model $P_{LS}$ which uses pre-trained attention weights and then use the weighted output of an encoder LSTM as the input to a softmax
This paper proposes a conditional GAN-based image captioning model.
Given an image, the generator generates a caption, and given an image
and caption, the discriminator/evaluator distinguishes between generated
and real captions. Key ideas:
- Since caption generation involves sequential sampling, which is
non-differentiable, the model is trained with policy gradients, with
the action being the choice of word at every time step, policy being
the distribution over words, and reward the score assigned by the
evaluator to generated caption.
- The evaluator's role assumes a completely generated caption as input
(along with image), which in practice leads to convergence issues. Thus
to accommodate feedback for partial sequences during training, Monte Carlo
rollouts are used, i.e. given a partial generated sequence, n completions
are sampled and run through the evaluator to compute reward.
- The evaluator's objective function consists of three terms
- image-caption pairs from training data (positive)
- image and generated captions (negative)
- image and sampled captions for other images from training data (negative)
- Both the generator and evaluator are pretrained with supervision / MLE, then
fine-tuned with policy gradients. During inference, evaluator score is used as
the beam search objective.
## Strengths
This is neat paper with insightful ideas (Monte Carlo rollouts for assigning
rewards to partial sequences, evaluator score as beam search objective),
and is perhaps the first work on C-GAN-based image captioning.
## Weaknesses / Notes
This paper proposes a framework where an agent learns to navigate a 2D maze-like
environment (XWORLD) from (templated) natural language commands, in the process
simultaneously learning visual representations, syntax and semantics of language and
performing navigation actions. The task is essentially VQA + navigation; at every step
the agent either gets a question about the environment or navigation command,
and the output is either a navigation action or answer. Key contributions:
- Grounding and recognition are tied together to be two versions of the same problem.
In grounding, given an image feature map and label (word), the problem is to find
regions of the image corresponding to word semantics (attention map); and in
recognition, given an image feature map and attention, the problem is to assign
a word label. And thus word embeddings (for grounding) and softmax layer weights
(for recognition) are tied together. This enables transferring concepts
learnt during recognition to navigation.
- Further, recognition is modulated by question intent. For e.g. given an
attention map that highlights an agent's west, should it be recognized as
'west', 'apple' or 'red' (location, object or attribute)? It depends on what
the question asks. Thus, GRU encoding of question produces an embedding mask
that modulates recognition. The equivalent when grounding is that word embeddings
are passed through fully-connected layers.
- Compositionality in language is exploited by performing grounding and
recognition by sequentially (softly) attending to parts of a sentence and
grounding in image. The resulting attention map is selectively combined
with attention from previous timesteps for final decision.
## Weaknesses / Notes
Although the environment is super simple, it's a neat framework and it is useful
that the target is specified in natural language (unlike prior/concurrent work
e.g. Zhu et al., ICRA17). The model gets to see a top-down centred view of the
entire environment at all times, which is a little weird.
This paper gets a face image and changes its pose or rotates it (to any desired pose) by passing the target pose as the input to the model.
They use a GAN (named DR-GAN) for face rotation. The gan has an encoder and a decoder. The encoder takes the image and gets a high-level feature representation. The decoder gets high-level features, the target pose, and some noise to generate the output image with rotated face.
The generated image is then passed to a discriminator where it says whether the image is real or fake. The disc also has two other outputs: 1- it estimates the pose of the generated image, 2) it estimated the identity of the person.
no direct loss is applied to the generator, it is trained by the gradient that it gets through discriminator to minimize the three objects: 1- gan loss (to fool disc) 2-pose estimation 3- identity estimation.
They use two tricks to improve the model:
1- using the same parameters for encoder of generator (gen-enc) and the discriminator (they observe this helps better identity recognition)
2- passing two images to gen-enc and interpolating between their high-level features (gen-enc output) and then applying two costs on it: 1) gan loss 2) pose loss. These losses are applied through disc, similar to above.
The first trick improves gen-enc and second trick improves gen-dec, both help on identification.
Their model can also leverage multiple image of the same identity if the dataset provides that to get better latent representation in gen-enc for a given identity.
I find these positive aspects about the paper:
1) face rotation is applied on the images in the wild, 2) It is not required to have paired data. 3) multiple source images of the same identity can be used if provided, 4) identity and pose are used smartly in the discriminator to guide the generator, 5) model can specify the target pose (it is not only face-frontalization).
Negative aspects:
1) face has many artifacts, similar to artifacts of other gan models.
2) The identity is not well-preserved and the faces seem sometime distorted compared to the original person.
They show the models performance on identity recognition and face rotation and demonstrate compelling results.
I like the idea proposed in this paper - training on a label-rich domain and transfer the representation to a label-limited domain, but would like to extend it to data more than faces such as transferring the object attributes.
A NLP paper.
> "conditional computation, achieving greater than 1000x improvements in model capacity with
only minor losses in computational efficiency on modern GPU clusters. We introduce
a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to
thousands of feed-forward sub-networks"
## Evaluation
* 1 billion word language modeling benchmark
* 100 billion word google news corpus
## Headline Summary
The problem of vanishing or exploding gradients when training recurrent neural networks can be partially overcome by enforcing an orthogonality constraint on the weight matrices between consecutive hidden states. This paper explores the trade-off between enforced orthogonality and the rate of learning by slowly increasing the extent to which the weight matrices are allowed to deviate from orthogonality. They find that a hard orthogonality constraint can be too restrictive and perseveres gradients at the cost of slowing learning.
### Why orthogonal matrices are likely to help with exploding gradients
For a neural network with N hidden layers we can write the gradient of the loss wrt to the weight matrix between hidden states $W_i$ as:
$ \frac{\partial L}{\partial W_i} = \frac{\partial a_i}{\partial w_i} \prod_j^N (\frac{\partial h_i}{\partial a_i} \frac{\partial a_i}{\partial h_i}) \frac{\partial L}{\partial a_{N+1}} = \frac{\partial a_i}{\partial w_i} \prod_j^N (D_i W_{j+1}) \frac{\partial L}{\partial a_{N+1}}$
where L is the loss function and D is the jacobian of the activation function. It's in this long chain of products that the gradient tends to either vanish or explode. This problem is particularly bad in recurrent nets where the chain is the length of the unrolled computation graph.
The norm of the derivative $\|\frac{\partial h_i}{\partial a_i} \frac{\partial a_i}{\partial h_i} \|= \|D_i W_{j+1}\| \leq \|D_i \| \| W_{j+1}\| \leq \lambda_D \lambda_W$
where $\lambda_{D/W}$ is the largest singular value of the given matrix. Its clear that if $\lambda$ is less than or greater than 1, that the norm of the gradient will grow or decay exponentially with length of the chain of products given above.
Constraining the weight matrices to be orthogonal ensures that the singular values are 1 and so perseveres the gradient norm.
### How to enforce the orthogonality constraints
The set of all orthogonal matrices from a continuos manifold known as the stiefel manifold. One way to ensure that the weight matrices remain orthogonal is to initialise them to be orthogonal and then to perform gradient optimisation on this manifold. The orthogonality of the matrix can be preserved by a Caley transformation outlined in the paper.
In order to allow the weight matrix to deviate slightly from the stiefel manifold, the authors took the SVD of $W = USV^T $ and parameterised the singular values. They used geodesic gradient descent to maintain the orthogonality of $U$ and $V$. The singular values were restricted to between $ 1 ^+_- m$ where $m$ is a margin by parameterisation with a sigmoid.
### Experiment Summary
They experiment on a number of synthetic and real data-sets with elman and factorial RNNs. The synthetic tasks are the hochrieter memory and addition tasks, classifying sequential mnist and the real task is a language modelling task using the Penn Tree Bank dataset.
In the synthetic memory tasks they found that pure othogonalisation hindered performance but that a small margin away from orthogonality was better than no orthogonal constraint.
On the mnist tasks, the LSTM outperformed all other versions of simple RNNs but the simple RNNs with orthogonal constraints did quite well. Orthonal initialisation outperformed identity and glorot initialisation.
On the language modelling task with short sentences, orthogonal constraints were detrimental but on the longer sentences beneficial. They did not compare to LSTM. They also found that orthogonal initialisation was almost as good as orthogonal constraints during learning suggesting that preserving gradient flow is most important early in learning.
### My Two Cents
This paper explores an interesting approach to learning long term dependencies even with very simple RNNs. However, as theoretically appealing as the results may be, they still fail to match the memory capacities of an LSTM so for the time being it doesn't seem that there are any major practical take aways.
#### Summary:
The main point of the paper is to show the automatic tranformation process of a java project to run on AWS lambda. For the transformation process a self developed tool named Podilzer is used to perform the tests. Further a comparison of the execution times and cost factors is made, to show if it's valuable to run Java functions on AWS lambda.
#### Good points:
The pipeline process is well described and good understandable. The developed tool called Podilizer implements this pipeline process and is also as open source project available. The experiments are also available on the openscience platform including scripts and code, this grantees repeatability of the conducted tests.
#### Major comments:
One of the main problems with FaaS in general is preserving the state of an application. This challenge is described well. I wished to get a bit more insight what other problematic functions exist and what the approach of transforming those would be.
The java projects which were used to conduct the results are available but not described in the paper. Therefore the transformation times are difficult to assess, before a study of the projects itself. Further the execution performance of the same applications were compared locally and on different cloud offerings. On AWS Lambda the execution performance is significantly higher then on all the other platforms. Probably the times also include network latency, therefore it would be also interesting to see the actual execution times on Lambda itself.
#### Minor comments:
Generally good grammar, some minor typos.
#### Recommendations:
Describe the problems of transforming existing functions in more extend. Include also the "real" execution times of the AWS Lambda functions, to have a better comparison between the run times.
Finally proofread and publish after corrections have been made
This paper learns deep local patch descriptor (for replacing SIFT) by hard negative mining using current mini-batch. It outperforms SIFT and deep competitors on Oxford5K and Paris6K retrieval datasets.
At the core of an actor-critic algorithm is an idea of approximating an objective function $Q(s, a)$ (or Q function) with a trainable function $Q_\theta$ called a critic. An actor $\pi_\phi$ is then trained to maximize $Q_\theta$ instead of the original $Q$. Often, a pair of $Q_\theta$ and $\pi_\phi$ are trained separately for each task $\mathcal{T}$. In this paper, the authors cleverly propose to share a single critic $Q_\theta$ across multiple tasks $\mathcal{T}_1, \ldots, \mathcal{T}_L$ by parametrizing it to be dependent on a task. That is, $Q_\theta(s, a, z)$, where $z$ is a task vector.
For simplicity, consider a supervised learning task (as in Sec. 3.2), where $Q^t$ is an objective function for the $t$-th task and takes as input a training example pair $(x, y)$. A single critic $Q_\theta$ is then trained to approximate $L$-many such objective functions, i.e., $\arg\min_{\theta} \sum_{t=1}^L \sum_{(x,y)} (Q^t(x,y) - Q_\theta(x,y,z^t))^2$. The task vector $z^t$ is obtained by a task encoder (TAEN) which takes as input a minibatch of training examples of the $t$-th task and outputs the task vector. The TAEN is trained together with the $Q_\theta$, and all the $L$ actors $\pi^t$.
Once the critic (or meta-critic, as referred to by the authors) is trained, a new actor $\pi_\phi^{L+1}$ can be trained based on a small set of training examples in the following steps. First, the small set of training examples are used to compute a new task vector $z^{L+1}$. Second, a new critic is computed: $Q_\theta(\cdot,\cdot,z^{L+1})$. Third, the new actor is trained to maximize the new critic.
One big lesson I learned from this paper is that there are different ways to approach meta-learning. In the context of iterative learning of neural nets, I've only thought of meta-learning as learning to approximate an update direction as from https://arxiv.org/abs/1606.04474, i.e., $\phi \leftarrow \phi + g_\theta(\phi, x, y)$. This paper however suggests that you can instead learn an objective function, i.e., $\phi \leftarrow \phi + \eta \nabla_{\phi} Q_\theta(x, \pi_{\phi}(x), z(D))$, where $z(D)$ is a task vector obtained from new data $D$.
This is interesting, as it maximally reuses any existing technique from gradient-based learning and frees the meta-learner from having to re-learn them again.
Imagine you make a neural network mapping a scalar to a scalar. After you initialise this network in the traditional way, randomly with some given variance, you could take the gradient of the input with respect to the output for all reasonable values (between about - and 3 because networks typically assume standardised inputs). As the value increases, different rectified linear units in the network will randomly switch on, drawing a random walk in the gradients; another name for which is brown noise.
However, do the same thing for deep networks, and any traditional initialisation you choose, and you'll see the random walk start to look like white noise. One intuition given in the paper is that as different rectifiers in the network switch off and on the input is taking a number of different paths though the network. The number of possible paths grows exponentially with the depth of the network, so as the input varies, the gradients become increasingly chaotic. **The explanations and derivations given in the paper are much better reasoned and thorough, please read those if you are interested**.
Why should we care about this? Because the authors take the recent nonlinearity [CReLU][] (output is concatenation of `relu(x)` and `relu(-x)`) and develop an initialisation that will avoid problems with gradient shattering. The initialisation is just to take your standard initialised weight matrix $\mathbf{W}$ and set the right half to be the negative of the left half ($\mathbf{W}_{\text{left}}$). As long as the input to the layer is also concatenated, the left half will be multiplied by `relu(x)` and the right by `relu(-x)`. Then:
\mathbf{W}.\text{CReLU}(\mathbf{x}) = \begin{cases} \mathbf{W}_{\text{left}}\mathbf{x} & \text{ if } x > 0 \\ \mathbf{W}_{\text{left}}\mathbf{x} & \text{ if } x \leq 0\end{cases}
Doing this allows them to train deep networks without skip connections, and they show results on CIFAR-10 with depths of up to 200 exceeding (slightly) a similar resnet.
[crelu]: https://arxiv.org/abs/1603.05201
#### About APAD ####
_This summer I've been Interning at an AI lab at BCM, working under
Ankit Patel in his incredible Neuroscience-meets-Deep-Learning group.
This field is moving faster than anything else out there. Keeping intro short,
so full post + inline pictures are on my [blog](http://rayraycano.github.io/data%20science/tech/2017/07/31/A-Paper-a-Day-GEM.html)_
## Basics ##
* __Paper__: [Gradient Episodic Memory for Continuum Learning][GED]
* __Authors__: David Lopez-Paz, Marc'Aurelio Ranzato
* __Organizaitons__: Facebook AI Research (FAIR)
* __Topic__: Coninuum Learning
* __In One Senetence__: _Paz and Ranzato define sorely needed metrics to the
subfield of Continual Learning while developing a gradient-inspired update rule
that avoids catastrophic forgetting_
### Background ###
Paz and Ranzato's continuum learning targets a more general problem of
_catastrophic forgetting_, which the authors describe as "the poor ability of models to
quickly solve new problems, without forgetting previously acquired knowledge."
Recently this has been a hot topic in AI recently, as a flurry of
papers in early summer were released discussing this topic ([Elastic Weight Consolidation][EWC],
[PathNet][PathNet], [iCaRL][iCARL], [Sluice Network][Sluice Network], [Intelligent Synapses][Intelligent Synapses]).
Avoiding catastrophic forgetting and achieving nontrivial _backwards transfer_ (BT) and _forward transfer_ (FT) are major goals for
continual learning models, and in addition, general AI.
__Analogy Alert!__ _As Ankit explained to me originally:
If you know how to play tennis, your experience
*should* aid your ability to pick up ping pong (FT).
In addition, when you return to tennis, your aptitude
in tennis shouldn't decrease (some atheletes argue that they get better
at their primary sport because they've played secondary sports, i.e. BT)._
## Paper Accomplishments ##
1. Of the 5 papers mentioned above, 0 of them formally define metrics for a
continual learner.
2. The gradient-aligning update rule is quite clever and pretty cool.
### The Metrics ###
First, let's take a look at their formal definitions for FT and BT.
They're displayed below. The notation is a bit confusing, so I've done my
best to parse it.
[Backward And Forward Transfer LaTeX](https://www.dropbox.com/s/qrj6sxkfruj42uk/Screen%20Shot%202017-07-31%20at%204.56.05%20PM.png?dl=1)
T is the total number of tasks, enumerated from 1 to T. The bi vector is the random initialization for each task. I've omitted accuracy from this discussion because it seem too novel in the context of this paper
* Assume a fixed sequence of tasks (Numbered 1 through T)
* Forward transfer is the average accuracy of some task, task _i_, after each
task in the sequence preceding _i_ is completed.
* Record the score for task _i_ upon random initialization
* Learn task 1, record your score for task _i_. Learn task 2, record your
score for task _i_, etc., up until task _i-1_.
* Subtract from their score upon initialization and average these scores.
* Backwards Transfer is the average accuracy change for task _i_ after each
task afterwards has been completed
* Record the score for task _i_ after learning it
* Learn task _i+1_. Now record the score for task _i_. Learn task _i+2_. Record the
score for task _i_, etc.
* For each score of task _i_ that recorded after completeing _i_, subtract from the first
score for task _i_. Average these Scores
#### Gripes about the Metrics ####
In my opinion, these metrics don't generalize well. Continuum Learning (which I presume
is less general than _Continual_ Learning) specifies a sequence of tasks,
meaning it is sensitive to the order of tasks. In their experiments section,
they use tasks that theoretically don't depend on the order in which they're learned, so in the
scheme of their paper this point is moot. However, Continual Learning in general
has no specification on order. Other papers concerning this topic have not discussed
task curriculums at all, while this paper glosses over it.
__A metric I prefer: randomly sample from a pool of tasks
_n_ times. Learn these _n_ tasks in an arbitrary order. Lastly, evaluate accuracy on _i_ (for forward transfer).__
(This can be done over multiple trials to get a robust average)
### Gradient-Aligned Updates ###
For those new to Machine Learning, much of Deep Learning is powered by the
[__backpropagation algorithm__][backprop]. This algorithm calculates an update that
will improve the accuracy for the problem based on an error metric. It does this by calculating what's
called a gradient.
__Analogy Alert!__ : _You shoot a dart at a board. You shot low by some distance __d__.
You correct your mechanics, backwards reasoning from the missed distance, to your
release point, and from there perhaps your throwing velocity.
You can think of these corrections as the gradient, and the linked modification of
all the preceding components as an implementation
of the [chain rule][chainrule]. Disclaimer:
There is no evidence that the brain implements backpropagation_
So what does Gradient Episodic Memory (GEM) do exactly? Let's start with the Memory
part and go from there
* _Memory_: Recall those sequence of tasks we mentioned earlier? Well for each of those
tasks, let's make sure we don't forget them. We'll keep a portion of them in memory.
* _Episodic_: Let's replay these memories to make sure we're not damaging our accuracy
on these tasks when we learn new ones. By playing them over again, we're basically going through an episode
* _Gradient_: When we look at the episode again, let's make sure the gradient doesn't
go the wrong way. What this means is: let's not unlearn what we learned on the
previous task.
### So How is it Done? ###
[Gradient Update Equation][Gradient Update Equation]
g is the gradient for the current task, while gk is the gradient for each previous task, calculated over the episodes in memory (Mk). The big < > notation is a dot product operator
Dot Products! In order for a gradient update to take place, they compute the dot product
of the current learning task with all the previous tasks in memory. The update is allowed
to take place if the gradient is greater than or equal to 0 for all the episodes. This translates
into constraining your update for one task to not conflict with an update for the previous task.
What if the gradient is going the wrong way? Paz and Ronzato take this gradient update
and project it to the closest possible vector that doesn't go the wrong way
(The proof is in the pudding, eqn. 8 - 13 in the paper. It formulates the optimization as a projection
on to a cone).
[Gradient Update Projection Example][Gradient Update Projection Example]
Above shows the graphical representation of the gradient update conditions, with the blue
line being the update for the first task, while the red line is the gradient update for the current task. The right side
shows an approximation of the optimized projection for the gradient when the dot product is negative.
### Does it work? ###
Yes. Well, kind of.
The focus of this paper was to minimize Backwards Loss (aka
maximize Backwards Transfer). In this sense, they appear to succeed. However,
the small improvements lack error bars, making an unconvincing case (#DLNeedsErrorBars).
Forward Transfer is negligible on all but one experiment (there were three total).
[Results from Paper]
The plots above show performance. The right hand side demonstrates
the accuracy on the first task as the consequent tasks are learned, which each different
colored bar indicating a start to learning for a new task.
#### Knitpicking ####
* The experiments compare against Elastic Weight Consolidation (EWC). However,
EWC was tested and optimized for Reinforcement Learning and Atari Games. I wonder
if an earnest job of optimizing EWC for the tasks at hand was done.
* There is still no metric for parameter conservation as a result of continual/shared learning.
A curve showing the change in accuracy across a set of tasks while increasing
the size of the overall network would be nice. It would be interesting to compare
all the papers on this metric. You could also evaluate the similarity of tasks
(or how well a network learns similarities in tasks) through this method.
### Summary ###
Cool Method. Nice Paper. Less than satisfying results. But in general a
solid step forward for continual learning/overcoming catastrophic
[EWC]: https://arxiv.org/pdf/1612.00796.pdf
[PathNet]: https://arxiv.org/pdf/1701.08734.pdf
[iCARL]: https://arxiv.org/pdf/1611.07725.pdf
[Sluice Network]: https://arxiv.org/pdf/1705.08142.pdf
[Intelligent Synapses]: https://openreview.net/pdf?id=rJzabxSFg
[Metrics]: https://www.dropbox.com/s/qrj6sxkfruj42uk/Screen%20Shot%202017-07-31%20at%204.56.05%20PM.png?dl=0
[Gradient]: https://www.dropbox.com/s/jye3b3mco5fs277/Screen%20Shot%202017-07-31%20at%204.55.52%20PM.png?dl=0
[Results]: https://www.dropbox.com/s/qvr95xydhtlnijw/Screen%20Shot%202017-07-31%20at%204.55.41%20PM.png?dl=0
[GED]: https://arxiv.org/pdf/1706.08840.pdf
[Gradient Update Equation]:https://www.dropbox.com/s/jye3b3mco5fs277/Screen%20Shot%202017-07-31%20at%204.55.52%20PM.png?dl=1
[Gradient Update Projection Example]: https://www.dropbox.com/s/jkdkk8bmz6btl77/Screen%20Shot%202017-07-31%20at%207.36.14%20PM.png?dl=1 "Projection Update"
[Results from Paper]:https://www.dropbox.com/s/qvr95xydhtlnijw/Screen%20Shot%202017-07-31%20at%204.55.41%20PM.png?dl=1
Kind of a response/deeper dive into the durret/klein "easy victories" paper. Suggests that a) lexical features they used ("easy victories") are very prone to overfitting. They first show that several state of the art systems that use lexical features, trained on CoNLL data, perform poorly on wikiref, which was annotated using the same guidelines. Meanwhile the stanford sieve system performs about the same on both.
Then they show that a high percentage of gold standard linked headwords in the test set have been seen in the training set, and that a much lower percentage of errors are in the training set, implying that lexical features just allow you to memorize what kinds of things can be linked.
They suggest development of robust features, including using embeddings as lexical features, using lexical representations only for context, and on the evaluation side, using test sets that are different domains than the training set.
In this race for getting that extra few % improvement for a '*brand-new'* paper, this paper brings a fresh air by posing some very pertinent questions supported by rigorous experimental analysis. Its an ICCV 2017 paper. The paper talks about understanding activities in videos – both from activity classification and detection perspective. In doing so, the authors examined several datasets, evaluation metrics, algorithms, and pointed to possible future directions worthy of exploring. The default choice in terms of the dataset is Charades. Other than this, multiTHUMOS, THUMOS and ActivityNet are used as and when required. The activity classification/detection algorithms analyzed, are two-stream, improved dense trajectories (IDT), LSTM on VGG, actionVLAD and temporalfields.
The paper starts with the very definition of action. To quote *"When we talk about activities, we are referring to anything a person is doing, regardless of whether the person is intentionally and actively altering the environment, or simply sitting still".* This is a complementary perspective than what the community has perceived as action so far - *"Intentional bodily motion of biological agents"* [1]. The paper generalizes this notion and advocates that bodily motion is not indispensable to define actionness (*e.g.*, 'watching the tv', 'Lying on a couch' hardly consist of a bodily motion). Analysis of motion's role in understanding activity has played a major role later in the paper. Let's see some of the major questions that the authors explored in this paper.
1. "Only verbs" can make actions ambiguous. To quote, - "Verbs such as 'drinking' and 'running' are unique on their own, but verbs such as 'take' and 'put' are ambiguous unless nouns and even prepositions are included: 'take medication', 'take shoes', take off shoes'". The experiments involving both human (sec 3.1) and activity algorithms (sec 4.1) shows that given the verb less confusion arises when the object is mentioned ('holding a cup' vs 'holding a broom'), but given the object, confusion is more among different verbs ('holding a cup' vs 'drinking from a cup'). All the current algorithms are shown to have significant confusion among similar action categories, both in terms of verbs and objects. In fact, for a given category, the more categories share the object or verb, the worse is the accuracy.
2. The next study, to me, is the most important one. It's about the long-standing concern of whether activities have clear and universal boundaries. The human study shows that, in fact, it is ambiguous. Average human agreement with ground truth is only 72.5% IOU for Charades and 58.7% IOU for MultiTHUMOS. In a natural course of action, the authors wanted to see if this ambiguity is affecting the evaluation performance of the algorithms. For this purpose, they relaxed the ground truth boundary to be more flexible (sec 3.2) and then evaluated the performance of the algorithms. The surprising fact is that this relaxation did not improve the performance much. The authors opined that despite boundary ambiguity current datasets allow current algorithms to understand and learn from the temporal extent of activities. I must say, I did not expect that ambiguity in temporal boundary will have this insignificant effect on the localization performances. In addition to the conclusion as drawn by the authors, this can be caused by another issue. The (bad) effect of other things are so large that the correction due to boundary ambiguity can't change the performance much. What I mean is - it may not be that the datasets are sufficient but the algorithms are suffering from other flaws much more than they are suffering from the boundary ambiguity.
3. Another important question that the authors dealt with is – how does the amount of labeled training data affect the performance. The broad finding goes with the common knowledge of - "more data means better performance". However, there are a plethora of finer equally important insights that the authors pointed out. The amount of data does not affect all categories equally, especially for a dataset with long-tailed distribution of classes. Smaller categories are more affected. In addition, activities with more similar categories (that share the same object/verb) also get affected much more than their counter parts. The authors end the subsection (sec 4.2) with an observation that improvement can be made by designing algorithms that are better able to make use of the wealth of data in small categories than in large ones.
4. The authors did a thorough analysis of the role of temporal reasoning (motion, continuity, and temporal context) for activity understanding. The very first finding is that current methods are doing better for longer activities than shorter ones. Another common notion (naive temporal smoothing of the predictions helps improve localization and classification) is also verified.
5. An action is almost invariably related to persons. So, the authors tried to see if person based reasoning helps. For that, they experimented with removing the person from the scene, keeping nothing but the person etc. They also examined how diverse are the datasets in terms of human pose and if injecting human pose information helps the current approaches. The conclusion was that person based reasoning helps and the nature of the videos require the activity understanding approaches to harness pose information for improved performance.
6. Finally, the authors try to see what aspects help most if that aspect is solved perfectly with an oracle. The oracles include perfect object detection, perfect verb identification and so on. It varies for datasets to some extent but, in general, the finding was that all the oracles help, some more some less.
I think this is a much-needed work that would help the community to ponder over different avenues of activity understanding in videos to design better systems.
[1]. Wei Chen, Caiming Xiong, Ran Xu, Jason J. Corso, Actionness Ranking with Lattice Conditional Ordinal Random Fields, CVPR 2014.
A Critical Paper Review by Alex Lamb:
Convert MR scans to CT scans.
General Approach
Unpaired brain CT:MR images.
The dataset contains both CT and MR scans of same patient taken on the same day.
The volumes are aligned using mutual information and contain some local minor misalignments.
Train the following models:
1. Syn_ct: CNN: MR -> CT
2. Syn_mr: CNN: CT -> MR
3. Dis_ct: classify real and synthetic CT images (result of Syn_ct)
4. Dis_mr: classify real and synthetic MR images. Syn_mr(Syn_ct(MR Image))) or Syn_mr(CT image)
"The SE module can learn some nonlinear global interactions already known to be useful, such as spatial normalization. The channel wise weights make it somewhat more powerful than divisive normalization as it can learn feature-specific inhibitions (ie: if we see a lot of flower parts, the probability of boat features should be diminished). It also has some similarity to bio inhibitory circuits." By jcannell on reddit
Slides: http://image-net.org/challenges/talks_2017/SENet.pdf
Summary by the author Jie Hu:
Our motivation is to explicitly model the interdependence between feature channels. In addition, we do not intend to introduce a new spatial dimension for the integration of feature channels, but rather a new "feature re-calibration" strategy. Specifically, it is through learning the way to automatically obtain the importance of each feature channel, and then in accordance with this importance to enhance the useful features and inhibit the current task is not useful features.
The above figure is a schematic diagram of our proposed SE module. Given an input $x$, the number of characteristic channels is $c_1$, and the characteristic number of a characteristic channel is $c_2$ by a series of convolution and other general transformations. Unlike traditional CNNs, we then re-calibrate the features we received in the next three operations.
The first is the Squeeze operation, we carry out the feature compression along the spatial dimension, and turn each two-dimensional feature channel into a real number. The real number has a global sense of the wild, and the output dimension and the number of input channels Match. It characterizes the global distribution of responses on the feature channel, and makes it possible to obtain a global sense of the field near the input, which is very useful in many tasks.
Followed by the Excitation operation, which is a mechanism similar to the door in a circular neural network. The weight is generated for each feature channel by the parameter $w$, where the parameter w is learned to explicitly model the correlation between the feature channels.
Reddit thread: https://www.reddit.com/r/MachineLearning/comments/6pt99z/r_squeezeandexcitation_networks_ilsvrc_2017/
"Using the "SELU" activation function, you get better results than any other activation function, and you don't have to do batch normalization. The "SELU" activation function is:
if x<0, 1.051\*(1.673\*e^x-1.673) if x>0, 1.051\*x"
Source: narfon2, reddit
import numpy as np
def selu(x):
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale*np.where(x>=0.0, x, alpha*np.exp(x)-alpha)
Source: CaseOfTuesday, reddit
Discussion here: https://www.reddit.com/r/MachineLearning/comments/6g5tg1/r_selfnormalizing_neural_networks_improved_elu/
Video generation of human motion given:
1. Single appearance reference image
2. Skeleton motion sequence
* KTH - grayscale human actions
* Human3.6M - color multiview human actions
Conditional GANs.
The authors try both Stack GAN and Siamese GAN.
The later provides better result.
Isn't using a full sequence of human skeleton motion considered more then a "hint"?
Predict human motion from static image
1. 2d pose sequence generator
2. convert 2d pose to 3d skeleton
3 Step training strategy
1. Train human 2d pose extractor using annotated video with 2d joint positions
2. 3d skeleton extractor: project mocap data to 2d and use as ground truth for training the 2d->3d skeleton converter
3. Full network training
1. Penn Action - Annotated human pose in sports image sequences: bench_press, jumping_jacks, pull_ups...
2. MPII - human action videos with annotated single frame
3. Human3.6M - video, depth and mocap. action include: sitting, purchasing, waiting
On the following tasks:
1. 2D pose forecasting
2. 3D pose recovery
Motion prediction
auto-conditioned LSTM - an LSTM network that uses only fraction of the input timestamps, but all of the outputs (a little bit similar to keyframes).
test 1123


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Tuberculosis Patient's Voyage Stirs Debate Over Security Issues
(See Corrections & Amplifications item below.)
Billions of dollars spent on tougher airline security and border protection proved incapable of stopping a globe-trotting tuberculosis patient from entering the U.S. last week, in large part because public-health issues haven't been treated as national-security issues, according to homeland security officials and experts.
The slip, in which Centers for Disease Control and...

subset: commoncrawl


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When Facebook named Bing as its fifth Instant Personalization partner today, the social media site appeared to significantly raise the worth of "likes" and check-ins that originate on its platform. And the companies' expanded search partnership gives big brands and local businesses all the more reason to invest in increasing their Facebook presence.
Beginning today, said Yusuf Mehdi, SVP of online audience business at Microsoft, Bing.com will start displaying pictures and names of a searcher's Facebook friends who have either "liked" a business or checked in at one via Facebook Places. In short, it's bringing a similar Facebook imprint in terms of look and feel to Bing that has for months been seen on other Instant Personalization partner sites – Yelp, Microsoft's Docs.com, Pandora, and Rotten Tomatoes.
"We are not just about blue links anymore," Mehdi said.
Search links for businesses with "likers" and check-ins will appear in Facebook's "Web Results" section below links for People, Pages, and Groups. Facebook CEO Mark Zuckerberg, on hand for the announcement at Microsoft's Redmond, WA-based headquarters, talked about how people react socially online similarly to offline. "Seeing friends' faces and names, we are hard-wired to have that influence us," he said.
The CEO is awarding Facebook.com advertisers who have been spending money with the primary purpose of increasing their "likers" base. To be clear, the more "likers" accrued by companies, the more Bing searches for their brand will appear with Facebook-driven social context. According to comScore's most recent statistics, Bing currently accounts for 11.2 percent of the marketplace. It trails Google (66.1 percent) and Yahoo (16.7).
Further, Mehdi explained that if a Bing.com user searched for a restaurant and is served a link with relevant Facebook friends' check-in data next to it, the user will be able to ping them without changing websites to ask if they enjoyed eating there. Therefore, interestingly, Bing.com appearances should theoretically positively and negatively affect some search advertisers from now on.
At any rate, the deal – the terms of which were not disclosed – could be seen as a shot across the bow to Foursquare and local businesses that have been staying loyal to it and other geo-social platforms instead of migrating efforts to Facebook Places. Now, getting people to check in on Places will extend the reach of such an action beyond Facebook.com and to Bing's search share.
Meanwhile, attempting to address privacy concerns, Mehdi said that Bing.com visitors will be greeted with a "No Thanks" option the initial five times that they next visit the site. After that, they will be able to turn the application on or off within their Facebook privacy controls in the same way as the other Instant Personalization sites.
Facebook and Microsoft first partnered on search back in March, when the social site unveiled the "Web Results" feature. Zuckerberg said that expanding Facebook's search relationship with Microsoft and Bing makes a lot of sense. And the world's most prominent young CEO may have taken at least a slight jab at looming Internet advertising competitor and search giant Google.
"I couldn't think of anyone better to work with to build the next generation of search," Zuckerberg said.
Related reading
A guide to five Facebook ad types that will reach your marketing objectives
Facebook has announced that it is planning to withdraw the ability to boost a list of post types from its available ad formats, as they didn't seem to align with most advertisers' objectives. If you're looking to reconfigure your Facebook advertising strategy, what options remain, and which are the most effective?
Facebook Watch and the battle for digital TV dollars
Facebook's announcement of its Watch video service is a clear bid to tap into the lucrative TV market. How will it affect digital ad spend, and what do marketers need to know?
What YouTube's new in-app messaging feature means for brands
YouTube has decided to join the messaging trend with an in-app chatting service, allowing users to start a conversation directly through YouTube's mobile app. How can brands take advantage of the new social approach?
Five ways businesses can make their physical locations more Instagram-friendly
With more than 700 million monthly active users, businesses are paying more attention to Instagram than ever before. In fact, the photo-centric service is so popular that in an effort to make themselves more attractive to Instagrammers, some businesses are designing their physical spaces to be more photogenic. Here's how.


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Sirvoy plans to develop an internal POS system. The approximate date for this POS is set to take place later part of 2017.
Sirvoy can already be used as an Epos for occasional extra charges that could be added to a guest's booking. These transactions can be applied by going into the guest's booking details and clicking "Add extras", or by adding regular "Extras". You can find more information about adding custom charges at this support page.
If you need an Epos, or POS, for regular sales, as in a shop, or a restaurant, and you want to be able to sell to people who are not your guests, then you would need a dedicated Epos system, rather than using Sirvoy for that purpose, until our own restaurant POS system is functioning.


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Support Us!
Have a look at this fun, short fundraising flashmob video filmed in Deptford Market- a collaboration between our Street Dance Crew, our over 55's company 'The Supertroupers' and extremely generous local film makers.
And listen to their stories about being with Montage…
Montage Theatre Arts flashmob from Montage Theatre on Vimeo.
Montage Theatre Arts is a registered charity (no. 1103460) set up in 1998 offering excellent and vital opportunities for children, young people and adults from the local community and is Lewisham's only revenue-funded arts organisation in the Brockley Ward. Our inclusive courses promote confidence, well-being and create an important sense of belonging to a community. MTA are able to provide affordable opportunities due to funding from various sources and generous donations.
There are several ways in which you can help – we appreciate all the support we receive and look forward to hearing from you. One of the easiest ways is via Paypal. You can donate right now, just click the button below.


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Blended Learning in Finance: Comparing Student Perceptions of Lectures, Tutorials and Online Learning Environments Across Different Year Levels†
- †
This research was made possible by a Faculty Research Grant (2006) of $9500 awarded by the Faculty of Business and Law, Deakin University.
School of Accounting, Economics and Finance, Deakin University, 221 Burwood Highway, Burwood, Melbourne, VIC 3125, Australia. Email: email@example.com
In modern pedagogy, a blended approach is used comprising both face-to-face and online learning. This study investigates how undergraduate students majoring in finance view the different learning environments, and evaluates the changes in perception over the three years of the degree after controlling for gender, age, international/domestic student and English as a first language. Using a purpose designed survey instrument, students across the three years of undergraduate study rated the importance of lectures, tutorials and web-based learning environments in a blended learning model. The results indicate that there is still a strong preference for face-to-face learning. Additionally, there were significant differences in attitudes and perception by year level.


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It Is Finished! By Tom D Blakely ~ It is finished! Jesus cried It is finished, and it is. We are reconciled to God When we know Christ died for us. It is finished, we will know When … Continue reading


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Science Says Your Kids Should Go to School Later
Scientists say that for the health of your kids, they should go to school later.
Scientists say that for the health of your kids, they should go to school later.
One of the biggest events in our area includes legendary musical acts, hot air balloons and Binghamton's indigenous dish, Spiedies and it happens this weekend.
City of Binghamton officials have announced a $940,000 State Street Improvement Project which will change much of the landscape on State Street from Susquehanna Street to Hawley Street...
Attending the Spiedie Fest and Balloon Rally at Otsiningo Park? There are some things you need to know about parking and local traffic before you go.
The news: informing us and entertaining us, without even trying.
BC Transit has made changes to the Johnson City bus stop locations to make things safer for pedestrians and to ease traffic congestion around town.
If you've always wondered what it'd be like to rub shoulders with England's royal family, this is your chance because they're hiring!
Kelsea Ballerini announced her sophomore album 'Unapologetically' today, which will drop this November.
Hasbro's 'Tangled' the Series toys have long hair and don't care about anything but letting you create your own adventures with Disney's pop star princess.
Amy Schumer was originally supposed to play the role but backed out after being body-shamed.
Sounds like America's workers are phoning it in.
'It was the way you sang about those demons that made everyone fall in love with you,' the band's remaining members wrote.
Cue the "he's going to continue to search for the real killer" jokes.
'Phelps Vs Shark: Great Gold Vs Great White' will air July 23.


text: Seller: danmurphys_au (9,002) 98.5%, Location: Sydney, Ships to: AU, Item: 302127387222 Varietal: Australian Whisky, Brand Name: NED, Liquor Style: Premix Drinks, Size: 375mL, Alcohol Volume: 4.8, Country: Australia, Region: Australia, Standard Drinks: 1.4, Closure: Cans, Case size: 24, Brand: NED, Details: VISIT OUR STORE NED Whisky & Cola 4.8% 375mL case of 24 Australian Whisky Premix Drinks Varietal: Australian WhiskyLiquor Style: Premix Drinks* Delivery orders to a restricted post code in Australia will be not be processed due to current legislation and a full refund will be posted if required. $85.0per case of 24 Description: NED Whisky & Cola 24-can case. A ready to drink beverage created to suit the taste buds of true blue Australians. Steeped in history, the iconic figure represents a daring spirit. Free gift with every case purchased. --> Product Detail VarietalAustralian WhiskyLiquor StylePremix DrinksAlcohol Volume4.8RegionAustraliaClosureCans Brand NameNEDSize375mLCountryAustraliaStandard Drinks1.4Case size24 Categories Red Wine White Wine Champagne Dark Spirits Light Spirits Beer Help FAQs Delivery Returns & Refunds We accepts Dan Murphy's supports the Responsible Service of Alcohol. New South Wales: Liquor Act 2007. It is against the law to sell or supply alcohol to, or to obtain alcohol on behalf of, a person under the age of 18 years.Victoria: WARNING: Victoria Liquor Control Reform Act 1998: It is an offence to supply alcohol to a person under the age of 18 years (Penalty exceeds $17,000), for a person under the age of 18 years to purchase or receive liquor (Penalty exceeds $700). Western Australia: WARNING. Under the Liquor Control Act 1988, it is an offence: to sell or supply liquor to a person under the age of 18 years on licensed or regulated premises; or for a person under the age of 18 years to purchase, or attempt to purchase, liquor on licensed or regulated premises. South Australia: Liquor Licensing Act 1997, Section 113. Liquor must NOT be supplied to persons under 18. Queensland: Under the Liquor Act 1992, it is an offence to supply liquor to a person under the age of 18 years. For more specific legislation in your state or territory visit our Liquor Licensing Acts page. ABN 88 000 014 675. © 2016 Dan Murphy's. All rights reserved


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Creating built in custom cabinets and paneling to fill an awkward niche in a Huntington Beach home
Built in cabinets are the most common projects we work on and with every project we work with the home owners to figure out what kind of cabinetry best serves their needs for that space. Whether you want to create cabinetry to display certain items or a combination of display and storage use, we can help you plan and build cabinets that fit those requirements perfectly.
A perfect example of a more challenging built-in cabinet project is the one shown above – the recessed window and adjacent wall configurations made it difficult to decide what exactly to build into this space. The customers wanted to allow for convenient, accessible storage of AV components together with some display shelves and as much storage capacity as possible. All of this had to be incorporated into the cabinets built into the window-side wall, which in turn had to be seamlessly connected with the paneling that would be incorporated into the wall on the right side of the room. The resulting cabinetry and detailing for this project can be seen in the gallery below:
Huntington Beach Built In Cabinets & Paneling Project
Custom cabinets and paneling built in to a niche and adjacent walls for a Huntington Beach residence. Mahogany material was used for all the cabinetry, stained and lacquered to match another existing entertainment center cabinet.
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry3.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry-9.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry-10.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry-4.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry-5.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1371.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1401.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1402.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_huntington-beach-cabinetry-6.jpg]10Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1379.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1386.jpg]00Huntington Beach Custom Built In Cabinets, in Orange County CA
[img src=http://southerncaliforniacustomcabinetmakers.com/wp-content/flagallery/huntington-built-in-cabinets-paneling-project/thumbs/thumbs_img_1396.jpg]50Huntington Beach Custom Built In Cabinets, in Orange County CA
As you can see, we basically closed off the entire window area and used that side of the room to provide storage, display, and access to the AV components. All the components were housed on full-extension pull out shelving to allow adjustments without having to pull out the components out of the cabinet. The trickiest part was figuring out how to connect the wall of paneling with the cabinetry on the window side – you can see in the photos that a good amount of planning had to be done to get this connection down perfectly, particularly with the mouldings used for both sides of the woodwork.
The inside of the cabinetry on the window side was built using dark melamine material, which matched the natural wood exterior of the cabinets but allowed significant cost savings because with melamine no finishing is required inside the cabinetry. All the natural material was Mahogany wood, which looks amazing but is slightly more challenging to stain and lacquer than other wood species, especially when you are trying to match a specific color. At project completion, the transformation was fairly significant, with the room adapted to the customer's needs and beautiful natural Mahogany paneling and cabinetry covering two walls.
Stonebelt Cabinets
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