A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.
@cite_33: Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm--the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests the choice of activation function should always be cross-validated.
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@cite_36: 
@cite_29: Abstract All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Plausible models of human cognition should therefore exhibit similar patterns of gradual forgetting of old information as new information is acquired. Only rarely does new learning in natural cognitive systems completely disrupt or erase previously learned information; that is, natural cognitive systems do not, in general, forget ‘catastrophically'. Unfortunately, though, catastrophic forgetting does occur under certain circumstances in distributed connectionist networks. The very features that give these networks their remarkable abilities to generalize, to function in the presence of degraded input, and so on, are found to be the root cause of catastrophic forgetting. The challenge in this field is to discover how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks are examined. The review will consider how the brain might have overcome this problem and will also explore the consequences of this solution for distributed connectionist networks.
@cite_1: Catastrophic forgetting occurs when connectionist networks learn new information, and by so doing, forget all previously learned information. This workshop focused primarily on the causes of catastrophic interference, the techniques that have been developed to reduce it, the effect of these techniques on the networks' ability to generalize, and the degree to which prediction of catastrophic forgetting is possible. The speakers were Robert French, Phil Hetherington (Psychology Department, McGill University, het@blaise.psych.mcgill.ca), and Stephan Lewandowsky (Psychology Department, University of Oklahoma, lewan@constellation.ecn.uoknor.edu).
@cite_39: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
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@cite_40: 
@cite_23: Resume Les procedures d'apprentissage de descente de gradient sont le plus souvent utilisees dans la modelisation de reseaux neuronaux. Lorsque ces algorithmes (e.g., la retropropagation) sont appliques a des tâches d'apprentissage sequentiel, un defaut majeur, appele oubli catastrophique (ou interference catastrophique), survient generalement: lorsqu'un reseau ayant deja appris un premier ensemble d'items est ensuite entraine sur un deuxieme ensemble d'items, alors l'information nouvellement apprise peut completement detruire celle anterieurement apprise. Pour eviter ce defaut peu plausible, il est ici propose une architecture a deux reseaux neuronaux ou les nouveaux items sont appris par un premier reseau conjointement avec des pseudo-items internes provenant d'un second reseau. Comme il est demontre que ces pseudo-items refletent la structure des items anterieurement appris par le premier reseau, le modele implemente ainsi un processus de rafraichissement par l'ancienne information. Le point crucial est que ce mecanisme de rafraichissement repose sur des reseaux neuronaux reverberants qui n'ont besoin que de stimulations aleatoires pour operer. Le modele fournit ainsi un moyen de reduire d'une maniere spectaculaire l'interference retroactive tout en conservant la nature essentiellement distribuee de l'information et propose une hypothese originale et plausible sur la maniere de ≪ copier et coller ≫ la memoire distribuee au sein d'une structure cerebrale vers une autre.
@cite_2: Abstract The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.
@cite_10: Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a "fabric" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset.
@cite_12: This paper reviews the problem of catastrophic forgetting (the loss or disruption of previously learned information when new information is learned) in neural networks, and explores rehearsal mechanisms (the retraining of some of the previously learned information as the new information is added) as a potential solution. We replicate some of the experiments described by Ratcliff (1990), including those relating to a simple 'recency' based rehearsal regime. We then develop further rehearsal regimes which are more effective than recency rehearsal. In particular, 'sweep rehearsal' is very successful at minimizing catastrophic forgetting. One possible limitation of rehearsal in general, however, is that previously learned information may not be available for retraining. We describe a solution to this problem, 'pseudorehearsal', a method which provides the advantages of rehearsal without actually requiring any access to the previously learned information (the original training population) itself. We then sugge...
The recent success of (deep) neural networks can in large parts be attributed to their ability to learn not only classifiers but also suitable data representations @cite_39 @cite_28 @cite_36 @cite_10 , at least in the standard batch setting. First attempts to learn data representations in an incremental fashion can already be found in the classic neural network literature, @cite_23 @cite_1 @cite_29 @cite_12 . In particular, in the late 1980s McCloskey al @cite_19 described the problem of , the phenomenon that training a neural network with new data causes it to overwrite (and thereby forget) what it has learned on previous data. However, these classical works were mainly in the context of connectionist memory networks, not classifiers, and the networks used were small and shallow by today's standards. Generally, the existing algorithms and architectural changes are unable to prevent catastrophic forgetting, see, for example, Moe-Helgesen al's survey @cite_40 for classical and Goodfellow al's @cite_33 for modern architectures, except in specific settings, such as Kirkpatrick al's @cite_2 .