Discovering Implicit Classes Achieves Open Set Domain Adaptation

Jingyu Zhuang, Ziliang Chen, Pengxu Wei, Guanbin Li, Liang Lin

Abstract: In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specific belonging, existing methods indiscriminately regard them as a single class “unknown”. We challenge this broadly-adopted practice that may arouse unexpected detrimental ef-fects because the decision boundaries between the implicit categories have been fully ignored. Instead, we propose Self-supervised Class-Discovering Adapter (SCDA) that attempts to achieve OSDA by gradually discovering those implicit classes, then incorporating them to restructure the classifier and update the domain-adaptive features iteratively. SCDA performs two alternate steps to achieve implicit class discov-ery and self-supervised OSDA, respectively. By jointly op-timizing for two tasks, SCDA achieves the state-of-the-art in OSDA and shows a competitive performance to unearth the implicit target classes.