Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach.

翻译：多视角学习（MVL）在整合数据集的多角度信息以提升下游任务性能方面取得了显著成功。为增强MVL方法在开放环境中的实用性，本文研究了一种称为多视角类增量学习（MVCIL）的新范式，其中单一模型通过持续不断的多视角流增量式分类新类别，且无需访问先前的数据视角。然而，MVCIL面临旧信息灾难性遗忘与新概念学习干扰的双重挑战。为此，我们首先开发了一种基于随机化的表示学习技术用于特征提取，以确保其保持视角最优的独立工作状态（当属于同一类别的多个视角顺序呈现时）；随后，我们在由提取特征张成的正交融合子空间中逐一整合这些特征；最后，我们引入选择性权重巩固机制，在遇到新类别时实现无遗忘决策。在合成数据集与真实数据集上的大量实验验证了本方法的有效性。