Look-Ahead Selective Plasticity for Continual Learning
Abstract
Recent progress in contrastive representation learning has shown to yield robust representations that can avoid catastrophic forgetting in continual learning tasks. Most of these methods avoid forgetting by limiting changes in components of the deep neural network (DNN) that hold significant information about previously seen tasks. While these previous methods have been successful in preserving aspects of learned parameters believed to be most relevant for distinguishing previous classes, the retained parameters may be overfitted to seen data, leading to poor generalization even though “forgetting” is avoided. Inspired by modulation of early sensory neurons by top-down feedback projections of cortical neurons in perception and visual processing, we propose a class-incremental continual learning algorithm that identifies and attempts to preserve weights that contribute to the model performing well on new unseen classes by assessing their generalizability on a small predictive batch of the next episode of data. With experiments on popular image classification datasets, we demonstrate the effectiveness of the proposed approach and explain how using the model’s first encounter with new data to simulate a feedback signal for modulating plasticity of weights provides more information for training compared to using the loss value alone, and how it can guide the model’s learning through new experiences.