Reduce the Difficulty of Incremental Learning with Self-Supervised Learning

Abstract

Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before. Researchers have proposed methods to alleviate catastrophic forgetting; these methods only consider extracting features related to tasks learned before, suppression to extract features for unlearned tasks. As a result, when a deep learning model learns new tasks incrementally, the model needs to learn to extract the relevant features of the newly learned task quickly; this requires a significant change in the model's behavior of extracting features, which increases the learning difficulty. Therefore, the model is caught in the dilemma of reducing the learning rate to retain existing knowledge or increasing the learning rate to learn new knowledge quickly. We present a study aiming to alleviate this problem by introducing self-supervised learning into incremental learning methods. We believe that the task-independent self-supervised learning signal helps the learning model extract features not only effective for the current learned task but also suitable for other tasks that have not been learned. We give a detailed algorithm combining self-supervised learning signals and incremental learning methods. Extensive experiments on several different datasets show that self-supervised signal significantly improves the accuracy of most incremental learning methods without the need for additional labeled data. We found that the self-supervised learning signal works best for the replay-based incremental learning method.