Continual Learning and Catastrophic Forgetting

Abstract

In the recent years, lifelong learning (LL) has attracted a great deal of attention in the deep learning community, where it is often called continual learning. Though it is well-known that deep neural networks (DNNs) have achieved state-of-the-art performances in many machine learning (ML) tasks, the standard multi-layer perceptron (MLP) architecture and DNNs suffer from catastrophic forgetting [McCloskey and Cohen, 1989] which makes it difficult for continual learning. The problem is that when a neural network is used to learn a sequence of tasks, the learning of the later tasks may degrade the performance of the models learned for the earlier tasks. Our human brains, however, seem to have this remarkable ability to learn a large number of different tasks without any of them negatively interfering with each other. Continual learning algorithms try to achieve this same ability for the neural networks and to solve the catastrophic forgetting problem. Thus, in essence, continual learning performs incremental learning of new tasks. Unlike many other LL techniques, the emphasis of current continual learning algorithms has not been on how to leverage the knowledge learned in previous tasks to help learn the new task better. In this chapter, we first give an overview of catastrophic forgetting (Section 4.1) and survey the proposed continual learning techniques that address the problem (Section 4.2). We then introduce several recent continual learning methods in more detail (Sections 4.3–4.8). Two evaluation papers are also covered in Section 4.9 to evaluate the performances of some existing continual learning algorithms. Last but not least, we give a summary of the chapter and list the relevant evaluation datasets.