抗退化终身多视图聚类
Anti-Degradation Lifelong Multi-View Clustering
零空间投影知识库防退化技术可迁移到视觉模型的持续学习场景，例如在增量式多视图特征学习或跨模态表征对齐中，通过约束新知识更新方向与旧知识正交来避免灾难性遗忘。
原文依据 null-space-projection knowledge base anti-degradation technique, which ensures that new knowledge updates to the model only occur in directions orthogonal to the retained knowledge
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在现实场景中，新视图随时间不断被收集，形成动态视图流。为处理这种演化数据，需要终身多视图聚类框架而非静态模型。然而，视图间的大差异使得在学习新知识的同时保留已获取信息变得困难。现有少数方法使用一致性对齐或知识蒸馏来使新知识与旧知识对齐。然而，这些策略无法从根本上防止知识退化，因为新知识不可避免地干扰已学习的表示空间。为克服这一局限，我们提出一种新的抗退化终身多视图聚类（ALMC）框架。具体而言，我们创新性地提出零空间投影知识库抗退化技术，确保模型的新知识更新仅发生在与保留知识正交的方向上，从而防止知识的灾难性遗忘和聚类性能退化，并为此提供理论证明。在多个多视图基准数据集上的大量实验表明，在多视图聚类方面具有优越性能。
英文原文摘要 1213 字符
In real-world scenarios, new views are continuously collected over time, forming a dynamic view stream. To handle such evolving data, a lifelong multi-view clustering framework is needed instead of a static model. However, large discrepancies across views make it challenging to learn new knowledge while preserving previously acquired information. There are few methods use consistency alignment or knowledge distillation to align new knowledge with old ones. However, these strategies cannot fundamentally prevent knowledge degradation, since new knowledge inevitably interferes with the learned representation space. To overcome this limitation, we propose a new Anti-degradation Lifelong Multi-view Clustering (ALMC) framework. Specifically, we innovatively propose a null-space-projection knowledge base anti-degradation technique, which ensures that new knowledge updates to the model only occur in directions orthogonal to the retained knowledge, thus preventing catastrophic forgetting of knowledge and degradation of clustering performance, and provides theoretical proof for this. Extensive experiments on multiple multi-view benchmark datasets demonstrate superior performance in multi-view clustering.