用分层中心点记忆库提升在线持续自监督学习的稳定性
CLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning
- 用分层中心点聚类压缩记忆库，高效存储关键图像特征
- 在两个数据集上超越现有最优方法，尤其在不规则任务流下表现更优
- 适合需要长期学习且内存受限的自监督场景
在线持续自监督学习（OCSSL）旨在从无标签数据流中持续学习表征，不依赖任务边界信息且受内存限制。现有方法或依赖重放缓冲区利用潜在空间结构，或仅靠正则化。本文提出CLIMB（带智能记忆库的持续学习），同时结合两者。该方法采用层级中心点记忆机制，在总存储图像数受限的前提下，通过知识蒸馏对回放样本进行优化，抑制表征漂移。记忆库将相似图像聚为中心点，生成难以区分的对比学习样本，同时覆盖观测分布多样性。在Split CIFAR-100和Split ImageNet-100上的实验表明，无论标准基准还是新提出的非规则任务分布协议，CLIMB均优于当前最先进OCSSL方法。
原文摘要 · Abstract (English)
Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints. Existing methods rely either on replay buffers that exploit latent space structure, or on regularization alone. We present CLIMB (Continual Learning with Intelligent Memory Bank), which combines both simultaneously. Our method introduces a hierarchical centroid-based memory, bounded in total number of stored images, combined with knowledge distillation on replayed examples to limit representation drift. The memory groups similar images into centroids, providing hard-to-discriminate examples for contrastive learning while covering the diversity of observed distributions. Experiments on Split CIFAR-100 and Split ImageNet-100, on standard benchmarks from the state-of-the-art as well as a new protocol with irregular task distributions show that CLIMB outperforms state-of-the-art OCSSL methods.
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