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.
FearNet was shown to counteract CF on supposedly complex tasks such as CIFAR-100. However, FearNet was never trained on the CIFAR-100's raw images but rather the output of the first 49 weight layers (including the mean pooling layer) in ResNet-50 @cite_11 that had been pre-trained on ImageNet. This means that the classification is done by a much smaller multi-layer perceptron and the majority of the work has been pre-trained into the ResNet architecture. Subsequently, the autoencoder is not learning to reproduce CIFAR-100 images but rather the items' output from the ResNet architecture. Although this is a more difficult task than training on MNIST, this method is incomparable to training a convolutional neural network to classify the task from raw input.
@cite_11
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Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at this https URL .