Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://github.com/lijy373/CCLIS.

Authors

• Jiyong Li Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing
• Dilshod Azizov Mohamed bin Zayed University of Artificial Intelligence
• Yang Li
• Shangsong Liang Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Mohamed bin Zayed University of Artificial Intelligence

Keywords

• ML: Life-Long and Continual Learning
• ML: Representation Learning