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）在整合数据集多个角度的信息以提升下游任务性能方面取得了巨大成功。为使MVL方法在开放式环境中更具实用性，本文研究了一种称为多视角类增量学习（MVCIL）的新范式，其中单个模型通过持续流动的视图增量式地对新类别进行分类，且无需访问早期视图数据。然而，MVCIL面临旧信息灾难性遗忘与学习新概念时相互干扰的挑战。为解决这一问题，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，以保障各视图在呈现序列中多个属于同一类别的视图时处于各自视角最优的工作状态；随后，我们在由提取特征张成的正交融合子空间中对这些视图逐一整合；最后，针对遇到新类别时的无遗忘决策问题，引入选择性权重巩固机制。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。