Maintaining Plasticity in Deep Continual Learning
This addresses a fundamental limitation in deep learning for continual learning applications, such as adaptive AI systems, though it is incremental in improving existing methods.
The paper tackled the problem of deep neural networks losing their ability to learn new examples in continual learning settings, known as loss of plasticity, and found that L2-regularization with weight perturbation eased this issue, while a new algorithm, continual backpropagation, maintained plasticity indefinitely.
Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occurs continually. If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail to remember earlier examples. More fundamental, but less well known, is that they may also lose their ability to learn on new examples, a phenomenon called loss of plasticity. We provide direct demonstrations of loss of plasticity using the MNIST and ImageNet datasets repurposed for continual learning as sequences of tasks. In ImageNet, binary classification performance dropped from 89% accuracy on an early task down to 77%, about the level of a linear network, on the 2000th task. Loss of plasticity occurred with a wide range of deep network architectures, optimizers, activation functions, batch normalization, dropout, but was substantially eased by L2-regularization, particularly when combined with weight perturbation. Further, we introduce a new algorithm -- continual backpropagation -- which slightly modifies conventional backpropagation to reinitialize a small fraction of less-used units after each example and appears to maintain plasticity indefinitely.