The desire to run neural networks on low-capacity edge devices has led to the development of a wealth of compression techniques. Moonshine is a simple and powerful example of this: one takes a large pre-trained network and substitutes each of its convolutional blocks with a selected cheap alternative block, then distills the resultant network with the original. However, not all blocks are created equally; for a required parameter budget there may exist a potent combination of many different cheap blocks. In this work, we find these by developing BlockSwap: an algorithm for choosing networks with interleaved block types by passing a single minibatch of training data through randomly initialised networks and gauging their Fisher potential. We show that block-wise cheapening yields more accurate networks than single block-type networks across a spectrum of parameter budgets. Code is available at this https URL.
@cite_4: Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently evaluated solely by comparing their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we present a NAS evaluation framework that includes the search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the random policy outperforms state-of-the-art NAS algorithms; (ii) The results and candidate rankings of NAS algorithms do not reflect the true performance of the candidate architectures; and (iii) The widely used weight sharing strategy negatively impacts the training of good architectures, thus reducing the effectiveness of the search process. We believe that following our evaluation framework will be key to designing NAS strategies that truly discover superior architectures.
@cite_6: We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89 , which is on par with NASNet (, 2018), whose test error is 2.65 .
All of the above networks required significant engineering effort. The increasing complexity of neural network designs has encouraged the development of methods for automating (NAS). use an RNN to generate network descriptions and filter the options using reinforcement learning. Storing such a large quantity of possible networks is expensive, and their evaluation strategy utilised 450 GPUs over the course of 3 days. To address this, @cite_6 propose giving all models access to a shared set of weights, achieving similar performance to with a single GPU in less than 24 hours. Subsequent works have made extensive use of this technique . However, it has been observed that under the constrained architecture search space of the above methods, random architecture search provides a competitive baseline . In particular, @cite_4 show that weight sharing hampers the ability of candidate networks to learn and causes many NAS techniques to find suboptimal architectures.