Class Incremental Learning Method Integrating Balance Weight and Self-supervision

Abstract

In view of the catastrophic forgetting phenomenon of knowledge in class incremental learning in image classification, the existing class incremental learning methods focus on the correction of the unbalanced offset of the model classification layer, ignoring the offset of the model feature layer, and fail to solve the problem of the imbalance between the new and old samples faced by class incremental learning. Therefore, a new class incremental learning method is proposed, which is called balance weight and self-supervision (BWSS). BWSS designs an adaptive balance weight based on the low expectation of the old class in training, so as to expand the loss return proportion of the old class in the same data batch to correct the overall model offset. Then, BWSS introduces self-supervised learning to predict the rotation angle of the sample as an auxiliary task, so as to make the model have the expression ability of redundant features and common features to better support incremental tasks. Through the experimental comparison with the mainstream incremental class learning algorithms on the open datasets CIFAR-10 and CIFAR-100, it is proven that BWSS not only has better incremental performance on CIFAR-10 with fewer categories and more samples, but also has advantages on CIFAR-100 with more categories and fewer samples. Ablation experiments and feature visualization demonstrate that the proposed method is effective for the feature representation and incremental performance of the model. The final accuracy of BWSS’s 5-stage incremental task on CIFAR-10 reaches 76.9%, which is 5 percentage points higher than the baseline method.

Keywords

• incremental learning; self-supervision; problem of imbalance; distillation of knowledge