通过保持类判别结构实现无样本类增量学习
Exemplar-Free Class Incremental Learning via Preserving Class-Discriminative Structure
对做视觉分类/增量学习的人，这篇给出了一个可复用的正则化思路：在参数高效微调时同时约束类内协方差形状与样本-旧原型夹角，可直接插到基于预训练 ViT 的增量学习流程里缓解表征坍缩。
原文依据 catastrophic forgetting stems from the simultaneous deterioration of both intra-class and inter-class structures
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无样本类增量学习（EFCIL）旨在使模型能够在不保留先前任务样本的情况下顺序学习新类别。尽管近期方法利用预训练模型与参数高效微调来缓解遗忘，但它们往往忽视了一个关键的遗忘成因：类判别结构的坍缩。该结构由两个相互依存的组成部分构成：刻画各个类别形状的类内结构，以及刻画类原型之间全局几何关系的类间结构。我们揭示出，灾难性遗忘源于类内结构与类间结构的同时退化。为此，我们提出一个保持类判别结构的统一框架。该框架通过在迁移过程中重塑类均值与协方差以保持每个类别的形状，从而保持类内结构；并通过稳定样本与旧原型之间的角度关系来维持类间结构。大量实验表明，我们的框架在多个 EFCIL 基准上优于现有领先方法，验证了保持类判别结构对于缓解灾难性遗忘至关重要。
英文原文摘要 1299 字符
Exemplar-Free Class Incremental Learning (EFCIL) aims to enable models to learn new classes sequentially without retaining samples from previous tasks. While recent approaches leverage pre-trained models with parameter-efficient tuning to mitigate forgetting, they often overlook a crucial cause of forgetting: the collapse of the class-discriminative structure. This structure comprises two interdependent components: intra-class structure, which characterizes the shape of individual classes, and inter-class structure, which characterizes the global geometric relationships among class prototypes. We reveal that catastrophic forgetting stems from the simultaneous deterioration of both intra-class and inter-class structures. To address this, we propose a unified framework that preserves the class-discriminative structure. It preserves the intra-class structure by reshaping class means and covariances to preserve each class's shape during migration, and maintains inter-class structure by stabilizing angular relationships between samples and old prototypes. Extensive experiments demonstrate that our framework outperforms existing leading methods on multiple EFCIL benchmarks, validating that preserving the class-discriminative structure is crucial for mitigating catastrophic forgetting.