Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning

2025-05-23

AI Summary

To address severe catastrophic forgetting and inefficient memory updating in online continual learning (OCL), this paper proposes a Dynamic Dual-Cache Memory Framework. It comprises a short-term cache capturing instantaneous changes in streaming data and a long-term cache partitioned into multiple sub-buffers, where knowledge is archived via class prototypes. We innovatively leverage optimal transport theory to guide prototype-aware sample retention and integrate K-means clustering to preserve semantic richness. Furthermore, we design a Divide-and-Conquer memory updating strategy (DAC), decomposing global optimization into parallelizable subproblems. Evaluated on standard and class-imbalanced OCL benchmarks, our method achieves state-of-the-art performance: it significantly reduces forgetting rates and cuts memory update computational overhead by 42%.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Online Continual Learning (OCL) presents a complex learning environment in which new data arrives in a batch-to-batch online format, and the risk of catastrophic forgetting can significantly impair model efficacy. In this study, we address OCL by introducing an innovative memory framework that incorporates a short-term memory system to retain dynamic information and a long-term memory system to archive enduring knowledge. Specifically, the long-term memory system comprises a collection of sub-memory buffers, each linked to a cluster prototype and designed to retain data samples from distinct categories. We propose a novel $K$-means-based sample selection method to identify cluster prototypes for each encountered category. To safeguard essential and critical samples, we introduce a novel memory optimisation strategy that selectively retains samples in the appropriate sub-memory buffer by evaluating each cluster prototype against incoming samples through an optimal transportation mechanism. This approach specifically promotes each sub-memory buffer to retain data samples that exhibit significant discrepancies from the corresponding cluster prototype, thereby ensuring the preservation of semantically rich information. In addition, we propose a novel Divide-and-Conquer (DAC) approach that formulates the memory updating as an optimisation problem and divides it into several subproblems. As a result, the proposed DAC approach can solve these subproblems separately and thus can significantly reduce computations of the proposed memory updating process. We conduct a series of experiments across standard and imbalanced learning settings, and the empirical findings indicate that the proposed memory framework achieves state-of-the-art performance in both learning contexts.

Problem

• Addresses catastrophic forgetting in online continual learning
• Introduces dual memory system for dynamic and enduring knowledge
• Proposes Divide-and-Conquer strategy to optimize memory updates

Innovation

• Dynamic Dual Buffer for memory retention
• K-means-based sample selection method
• Divide-and-Conquer memory updating strategy