无标注的类增量学习
Annotation-Free Class-Incremental Learning
AI总结：
本文提出无标注类增量学习（AFCIL）框架，通过引入跨域世界知识辅助，实现无标注数据下的持续学习，超越现有基线方法，凸显世界知识在无标注持续学习中的重要性。
摘要：
尽管在持续学习领域取得了显著进展，从架构创新到巧妙的缓解灾难性遗忘策略，但现有大多数方法都基于一个强大但不现实的假设，即学习过程中始终有标注数据可用。然而，在现实场景中，数据往往按顺序到达且无标注，使传统方法变得不切实际。在本文中，我们重新审视持续学习的基本假设，并提出问题：当标签缺失且任务随时间逐步出现时，当前系统是否能够适应？为此，我们引入了无标注的类增量学习（AFCIL），一种更加现实且具有挑战性的范式，其中未标注的数据持续到达，学习者必须在没有监督的情况下逐步获取新类别。为了在AFCIL下实现有效的学习，我们提出了CrossWorld CL，一种跨域世界引导的持续学习框架，该框架将外部世界知识作为稳定的辅助来源纳入其中。该方法为每个下游类别检索语义相关的ImageNet类别，通过跨域对齐策略将下游和ImageNet特征映射，并最终引入一种新的回放策略。这种设计使模型能够在没有标注的情况下揭示语义结构，同时保持早期知识的完整性。在四个数据集中，CrossWorld-CL超越了CLIP基线和现有的持续学习和未标注学习方法，凸显了世界知识对无标注持续学习的益处。
英文摘要：
Despite significant progress in continual learning ranging from architectural novelty to clever strategies for mitigating catastrophic forgetting most existing methods rest on a strong but unrealistic assumption the availability of labeled data throughout the learning process. In real-world scenarios, however, data often arrives sequentially and without annotations, rendering conventional approaches impractical. In this work, we revisit the fundamental assumptions of continual learning and ask: Can current systems adapt when labels are absent and tasks emerge incrementally over time? To this end, we introduce Annotation-Free Class-Incremental Learning (AFCIL), a more realistic and challenging paradigm where unlabeled data arrives continuously, and the learner must incrementally acquire new classes without any supervision. To enable effective learning under AFCIL, we propose CrossWorld CL, a Cross Domain World Guided Continual Learning framework that incorporates external world knowledge as a stable auxiliary source. The method retrieves semantically related ImageNet classes for each downstream category, maps downstream and ImageNet features through a cross domain alignment strategy and finally introduce a novel replay strategy. This design lets the model uncover semantic structure without annotations while keeping earlier knowledge intact. Across four datasets, CrossWorld-CL surpasses CLIP baselines and existing continual and unlabeled learning methods, underscoring the benefit of world knowledge for annotation free continual learning.
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。
