May 14, 2024, 4:41 a.m. | Yang Yang, Nan Jiang, Yi Xu, De-Chuan Zhan

cs.LG updates on

arXiv:2405.06979v1 Announce Type: new
Abstract: Open-set Semi-supervised Learning (OSSL) holds a realistic setting that unlabeled data may come from classes unseen in the labeled set, i.e., out-of-distribution (OOD) data, which could cause performance degradation in conventional SSL models. To handle this issue, except for the traditional in-distribution (ID) classifier, some existing OSSL approaches employ an extra OOD detection module to avoid the potential negative impact of the OOD data. Nevertheless, these approaches typically employ the entire set of open-set data …

abstract arxiv classifier cs.lg data distribution issue performance robust semi semi-supervised semi-supervised learning set ssl supervised learning type

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