Web: http://arxiv.org/abs/2102.03526

Jan. 27, 2022, 2:11 a.m. | Kaidi Cao, Maria Brbic, Jure Leskovec

cs.LG updates on arXiv.org arxiv.org

A fundamental limitation of applying semi-supervised learning in real-world
settings is the assumption that unlabeled test data contains only classes
previously encountered in the labeled training data. However, this assumption
rarely holds for data in-the-wild, where instances belonging to novel classes
may appear at testing time. Here, we introduce a novel open-world
semi-supervised learning setting that formalizes the notion that novel classes
may appear in the unlabeled test data. In this novel setting, the goal is to
solve the class …

arxiv learning open semi-supervised learning supervised learning

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