多视图类增量学习
Multi-View Class Incremental Learning
AI总结：
本文提出多视图类增量学习范式，采用随机化特征提取、正交融合及选择性权重整合，解决增量分类中的灾难性遗忘，实验验证有效。
摘要：
多视图学习（MVL）在整合数据集多个视角的信息以提升下游任务性能方面取得了巨大成功。为使MVL方法在开放环境中更具实用性，本文研究了一种称为多视图类增量学习（MVCIL）的新范式，其中单个模型从连续不断的视图流中增量地对新类别进行分类，且无需访问早期视图的数据。然而，MVCIL面临旧信息灾难性遗忘和学习新概念时受到干扰的挑战。为解决此问题，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，以保证其各自视图最优的工作状态，在此过程中，属于一个类别的多个视图按顺序呈现；然后，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视图；最后，我们引入选择性权重整合，以在遇到新类别时实现无遗忘的学习决策。在合成和真实世界数据集上的大量实验验证了我们方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
