In general, neural networks are not currently capable of learning tasks in a sequential fashion. When a novel, unrelated task is learnt by a neural network, it substantially forgets how to solve previously learnt tasks. One of the original solutions to this problem is pseudo-rehearsal, which involves learning the new task while rehearsing generated items representative of the previous task s. This is very effective for simple tasks. However, pseudo-rehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items. We accomplish pseudo-rehearsal by using a Generative Adversarial Network to generate items so that our deep network can learn to sequentially classify the CIFAR-10, SVHN and MNIST datasets. After training on all tasks, our network loses only 1.67 absolute accuracy on CIFAR-10 and gains 0.24 absolute accuracy on SVHN. Our model's performance is a substantial improvement compared to the current state of the art solution.
A recent review of methods for overcoming CF concluded that current algorithms do not solve CF @cite_14 . They also found that EWC performed the best for learning multiple tasks and thus, we compare pseudo-recursal to EWC. The main contributions of our paper are; we show that pseudo-recursal can be used to overcome the CF problem in DNNs, sequentially learning CIFAR-10, SVHN and MNIST and we demonstrate that pseudo-rehearsal can be applied recursively to a separate classification and generative model. This architecture also satisfies the previously mentioned criteria. Although we limit ourselves to image classification in this paper, our techniques are applicable to other problems.
@cite_14
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In this paper we study the problem of image representation learning without human annotation. By following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection. To maintain the compatibility across tasks we introduce the context-free network (CFN), a siamese-ennead CNN. The CFN takes image tiles as input and explicitly limits the receptive field (or context) of its early processing units to one tile at a time. We show that the CFN includes fewer parameters than AlexNet while preserving the same semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we learn both a feature mapping of object parts as well as their correct spatial arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. Our proposed method for learning visual representations outperforms state of the art methods in several transfer learning benchmarks.