Imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning
Primary Area: transfer learning, meta learning, and lifelong learning
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Keywords: Continual Learning, Contrastive learning, Domain Incremental Learning
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Abstract: Continual Learning (CL) aims to learn and adapt continuously to new information while retaining previously acquired knowledge. Most state of the art CL methods currently emphasize class incremental learning. In this approach, class data is introduced and processed only once within a defined task boundary. However, these methods often struggle in dynamic environments, especially when dealing with imbalanced data, shifting classes, and evolving domains. Such challenges arise from changes in correlations and diversities, necessitating ongoing adjustments to previously established class and data representations. In this paper, we introduce a novel online CL algorithm, dubbed as Memory Selection with Contrastive Learning (MSCL), based on evolving intra-class diversity and inter-class boundary aware memory selection and contrastive data representation learning. Specifically, we propose a memory selection method called Feature-Distance Based Sample Selection (FDBS), which evaluates the distance between new data and the memory set to assess the representability of new data to keep the memory aware of evolving inter-class similarities and intra-class diversity of the previously seen data. Moreover, as the data stream unfolds with new class and/or domain data and requires data representation adaptation, we introduce a novel built-in contrastive learning loss (IWL) that seamlessly leverages the importance weights computed during the memory selection process, and encourages instances of the same class to be brought closer together while pushing instances of different classes apart. We tested our method on various datasets such as MNIST, Cifar-100, PACS, DomainNet, and mini-ImageNet using different architectures. In balanced data scenarios, our approach either matches or outperforms leading memory-based CL techniques. However, it significantly excels in challenging settings like imbalanced class, domain, or class-domain CL. Additionally, our experiments demonstrate that integrating our proposed FDBS and IWL techniques enhances the performance of existing rehearsal-based CL methods with significant margins both in balanced and imbalanced scenarios.
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Submission Number: 7268
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