Web: http://arxiv.org/abs/2206.07551

June 16, 2022, 1:11 a.m. | JoonHo Jang, Byeonghu Na, DongHyeok Shin, Mingi Ji, Kyungwoo Song, Il-Chul Moon

cs.LG updates on arXiv.org arxiv.org

Open-Set Domain Adaptation (OSDA) assumes that a target domain contains
unknown classes, which are not discovered in a source domain. Existing domain
adversarial learning methods are not suitable for OSDA because distribution
matching with \textit{unknown} classes leads to the negative transfer. Previous
OSDA methods have focused on matching the source and the target distribution by
only utilizing \textit{known} classes. However, this \textit{known}-only
matching may fail to learn the target-\textit{unknown} feature space.
Therefore, we propose Unknown-Aware Domain Adversarial Learning (UADAL), which …

arxiv domain adaptation learning lg open

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