Self-supervised Universal Domain Adaptation with Adaptive Memory Separation

Ronghang Zhu, Sheng Li

Abstract: Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain where both domains share a common label space and hold a private label space respectively. One of the most challenging goals in UniDA is to separate target samples from common classes and these from private classes without any prior knowledge on the target label space. In this paper, we propose a novel self-supervised adaptive memory network with consistency regularization for UniDA. The adaptive memory includes all target samples and source class centers, which dynamically divides target samples into common area, uncertain area, and unknown area based on the entropy. Our proposed framework jointly assigns a specific neighborhood to each target sample and clusters the target sample to its neighbor from the neighborhood. Most importantly, the proposed framework adopts consistency regularization that gradually makes the output of the classifier more reliable. This simple strategy is proved to be very effective for UniDA problem. Experimental results on two UniDA benchmarks demonstrate the effectiveness of our method.