Understanding Forgetting in Artificial Neural Networks

Dylan R. Ashley

Abstract: This thesis is offered as a step forward in our understanding of forgetting in artificial neural networks. ANNs are a learning system loosely based on our understanding of the brain and are responsible for recent breakthroughs in artificial intelligence. However, they have been reported to be particularly susceptible to forgetting. Specifically, existing research suggests that ANNs may exhibit unexpectedly high rates of retroactive inhibition when compared with results from psychology studies measuring forgetting in people. If this phenomenon, dubbed catastrophic forgetting, exists, then explicit methods intended to reduce it may increase the scope of problems ANNs can be successfully applied to. In this thesis, we contribute to the field by answering five questions related to forgetting in ANNs: How does forgetting in psychology relate to ideas in machine learning? What is catastrophic forgetting? Does it exist in contemporary systems, and, if so, is it severe? How can we measure a system’s susceptibility to it? Are the current optimization algorithms we use to train ANNs adding to its severity? This work answers each of the five questions sequentially. We begin by answering the first and second of the five questions by providing an analytical survey that looks at the concept of forgetting as it appears in psychology and connects it to various ideas in machine learning such as generalization, transfer learning, experience replay, and eligibility traces. We subsequently confirm the existence and severity of catastrophic forgetting in some contemporary machine learning systems by showing that it appears when a simple, modern ANN (multi-layered fully-connected network with rectified linear unit activation) is trained using a conventional algorithm (Stochastic Gradient Descent through backpropagation with normal random initialization) incrementally on a well-known multi-class classification setting (MNIST). We demonstrate that the phenomenon is a more subtle problem than a simple reversal of learning. We accomplish this by noting that both total learning time and relearning time are reduced when the multi-class classification problem is split into multiple phases containing samples from disjoint subsets of the classes. We then move on to looking at how we can measure the degree to which ANN-based learning systems suffer from catastrophic forgetting by constructing a principled testbed out of the previous multi-task supervised learning problem and two well-studied reinforcement learning problems (Mountain Car and Acrobot). We apply this testbed to answer the final of the five questions by looking at how several modern gradient-based optimization algorithms used to train ANNs (SGD, SGD with Momentum, RMSProp, and Adam) affect the amount of catastrophic forgetting that occurs during training. While doing so, we are able to confirm and expand previous hypotheses surrounding the complexities of measuring catastrophic forgetting. We find that different algorithms, even when applied to the same ANN, result in significantly different amounts of catastrophic forgetting under a variety of different metrics. We believe that our answers to the five questions constitute a step forward in our understanding of forgetting as it appears in ANNs. Such an understanding is essential for realizing the full potential that ANNs offer to the study of artificial intelligence.