提出新方法应对持续学习中的类别重复问题，性能更优。
Incremental Learning with Repetition via Pseudo-Feature Projection
- 通过动态调整特征提取器集合，利用类别重复进行对齐
- 在有重复场景下达到当前最优效果，经典场景也表现良好
- 适合真实世界中类别反复出现的持续学习任务
增量学习场景常无法反映真实推理需求——现实数据流往往任务边界模糊，且常见类别与概念重复出现。为此，本文提出包含部分重复和任务混合的新场景，其中重复模式是场景固有的，策略事先未知。研究了无实例增量学习方法在数据重复下的表现，并适配一系列前沿方法，在两种设置下进行分析与公平比较。进一步提出新方法Horde，可动态调整一组自洽的特征提取器，并利用类别重复实现对齐。所提方法在无重复的经典场景中表现竞争性，在有重复场景中达到当前最优水平。
原文摘要 · Abstract (English)
Incremental Learning scenarios do not always represent real-world inference use-cases, which tend to have less strict task boundaries, and exhibit repetition of common classes and concepts in their continual data stream. To better represent these use-cases, new scenarios with partial repetition and mixing of tasks are proposed, where the repetition patterns are innate to the scenario and unknown to the strategy. We investigate how exemplar-free incremental learning strategies are affected by data repetition, and we adapt a series of state-of-the-art approaches to analyse and fairly compare them under both settings. Further, we also propose a novel method (Horde), able to dynamically adjust an ensemble of self-reliant feature extractors, and align them by exploiting class repetition. Our proposed exemplar-free method achieves competitive results in the classic scenario without repetition, and state-of-the-art performance in the one with repetition.
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