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

• URL: http://arxiv.org/abs/2505.18101v1
• Date: Fri, 23 May 2025 16:57:04 GMT
• Title: Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning
• Authors: Congren Dai, Huichi Zhou, Jiahao Huang, Zhenxuan Zhang, Fanwen Wang, Guang Yang, Fei Ye,
• Abstract summary: Online Continual Learning (OCL) presents a complex learning environment in which new data arrives in a batch-to-batch online format.<n>We introduce 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.
• Score: 10.599650191041217
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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