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

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

URL: http://arxiv.org/abs/2403.04599v1
Date: Thu, 7 Mar 2024 15:47:52 GMT
Title: Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
Authors: Jiyong Li, Dilshod Azizov, Yang Li, Shangsong Liang
Abstract summary: We propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions. We also present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations.
Score: 14.25441464051506
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.