Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive Replay

TL;DR

This paper introduces Adaptive Contrastive Replay (ACR), a novel continual learning method that enhances out-of-distribution generalization and reduces forgetting by adaptively replaying misclassified samples and refining decision boundaries.

Contribution

The paper proposes ACR, a simple contrastive learning-based replay strategy that improves OOD generalization and balances stability-plasticity in continual learning.

Findings

ACR outperforms previous methods on Split CIFAR-100 by 13.41%.

ACR improves OOD generalization on Split Mini-ImageNet by 9.91%.

ACR enhances performance on Split Tiny-ImageNet by 5.98%.

Abstract

Machine learning models often suffer from catastrophic forgetting of previously learned knowledge when learning new classes. Various methods have been proposed to mitigate this issue. However, rehearsal-based learning, which retains samples from previous classes, typically achieves good performance but tends to memorize specific instances, struggling with Out-of-Distribution (OOD) generalization. This often leads to high forgetting rates and poor generalization. Surprisingly, the OOD generalization capabilities of these methods have been largely unexplored. In this paper, we highlight this issue and propose a simple yet effective strategy inspired by contrastive learning and data-centric principles to address it. We introduce Adaptive Contrastive Replay (ACR), a method that employs dual optimization to simultaneously train both the encoder and the classifier. ACR adaptively populates the replay buffer with misclassified samples while ensuring a balanced representation of classes and tasks. By refining the decision boundary in this way, ACR achieves a balance between stability and plasticity. Our method significantly outperforms previous approaches in terms of OOD generalization, achieving an improvement of 13.41\% on Split CIFAR-100, 9.91\% on Split Mini-ImageNet, and 5.98\% on Split Tiny-ImageNet.