ClaRe: Practical Class Incremental Learning By Remembering Previous Class Representations

TL;DR

ClaRe is a practical class incremental learning method that effectively mitigates catastrophic forgetting by remembering class representations, generating diverse instances, and maintaining high accuracy with minimal memory use.

Contribution

It introduces ClaRe, a novel approach that stores class representations to generate representative instances, reducing memory issues and improving generalization in class incremental learning.

Findings

Achieves low accuracy degradation over time on MNIST.

Does not require extensive memory for previous classes.

Outperforms prior methods in generalization and memory efficiency.

Abstract

This paper presents a practical and simple yet efficient method to effectively deal with the catastrophic forgetting for Class Incremental Learning (CIL) tasks. CIL tends to learn new concepts perfectly, but not at the expense of performance and accuracy for old data. Learning new knowledge in the absence of data instances from previous classes or even imbalance samples of both old and new classes makes CIL an ongoing challenging problem. These issues can be tackled by storing exemplars belonging to the previous tasks or by utilizing the rehearsal strategy. Inspired by the rehearsal strategy with the approach of using generative models, we propose ClaRe, an efficient solution for CIL by remembering the representations of learned classes in each increment. Taking this approach leads to generating instances with the same distribution of the learned classes. Hence, our model is somehow retrained from the scratch using a new training set including both new and the generated samples. Subsequently, the imbalance data problem is also solved. ClaRe has a better generalization than prior methods thanks to producing diverse instances from the distribution of previously learned classes. We comprehensively evaluate ClaRe on the MNIST benchmark. Results show a very low degradation on accuracy against facing new knowledge over time. Furthermore, contrary to the most proposed solutions, the memory limitation is not problematic any longer which is considered as a consequential issue in this research area.