Online Class-Incremental continual Learning (OCIL) addresses the challenge of continuously learning from a single-channel data stream, adapting to new tasks while mitigating catastrophic forgetting. Recently, Mutual Information (MI)-based methods have shown promising performance in OCIL. However, existing MI-based methods treat various knowledge components in isolation, ignoring the knowledge confusion across tasks. This narrow focus on simple MI knowledge alignment may lead to old tasks being easily forgotten with the introduction of new tasks, risking the loss of common parts between past and present knowledge.To address this, we analyze the MI relationships from the perspectives of diversity, representativeness, and separability, and propose an Enhanced Mutual Information (EMI) method based on knwoledge decoupling. EMI consists of Diversity Mutual Information (DMI), Representativeness Mutual Information (RMI) and Separability Mutual Information (SMI). DMI diversifies intra-class sample features by considering the similarity relationships among inter-class sample features to enable the network to learn more general knowledge. RMI summarizes representative features for each category and aligns sample features with these representative features, making the intra-class sample distribution more compact. SMI establishes MI relationships for inter-class representative features, enhancing the stability of representative features while increasing the distinction between inter-class representative features, thus creating clear boundaries between class. Extensive experimental results on widely used benchmark datasets demonstrate the superior performance of EMI over state-of-the-art baseline methods.

翻译：在线类增量持续学习（OCIL）旨在应对从单通道数据流中持续学习的挑战，在适应新任务的同时缓解灾难性遗忘。近年来，基于互信息（MI）的方法在OCIL中展现出良好性能。然而，现有基于MI的方法孤立地处理各类知识成分，忽视了跨任务的知识混淆。这种对简单MI知识对齐的狭隘关注可能导致旧任务随着新任务的引入而被轻易遗忘，存在丢失过去与当前知识间共有部分的风险。为解决这一问题，我们从多样性、代表性和可分离性三个角度分析互信息关系，并提出一种基于知识解耦的增强互信息（EMI）方法。EMI由多样性互信息（DMI）、代表性互信息（RMI）和可分离性互信息（SMI）构成。DMI通过考虑类间样本特征的相似性关系来多样化类内样本特征，使网络能够学习更通用的知识。RMI为每个类别总结代表性特征，并将样本特征与这些代表性特征对齐，使类内样本分布更紧凑。SMI为类间代表性特征建立互信息关系，在增强代表性特征稳定性的同时，加大类间代表性特征的区分度，从而在类别间建立清晰边界。在广泛使用的基准数据集上的大量实验结果表明，EMI方法优于当前最先进的基线方法。