重新思考开放世界半监督学习：分布不匹配与归纳推理
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
AI总结：
本文指出现有开放世界半监督学习依赖类分布一致和直推式评估的局限，通过引入分布不匹配与归纳推理设置，提出更符合真实场景的泛化训练、评估与学习策略。
摘要：
开放世界半监督学习（OWSSL）通过考虑未标记数据集中的新类别，将传统半监督学习扩展至开放世界场景。尽管近期OWSSL取得进展，但其成功常依赖两项假设：1）有标签与无标签数据集共享相同的平衡类先验分布，这在现实应用中通常不成立；2）使用无标签训练数据集进行评估，而此类直推式推理可能无法充分应对真实环境中的挑战。本文旨在通过解决上述问题来泛化OWSSL。我们的研究表明，与现有文献中的主流方法相比，实用的OWSSL可能需要不同的训练设置、评估方法和学习策略。
英文摘要：
Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the success often relies on the assumptions that 1) labeled and unlabeled datasets share the same balanced class prior distribution, which does not generally hold in real-world applications, and 2) unlabeled training datasets are utilized for evaluation, where such transductive inference might not adequately address challenges in the wild. In this paper, we aim to generalize OWSSL by addressing them. Our work suggests that practical OWSSL may require different training settings, evaluation methods, and learning strategies compared to those prevalent in the existing literature.
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。
