Dual Contrastive Universal Adaptation Network.

We study Universal Domain Adaptation (UniDA) problem, which is recently proposed. Different from existing domain adaptation (DA) methods, e.g., Closed set, Open set and Partial DA, UniDA does not need any prior knowledge about the overlap across the source and target label sets. We have two challenges in UniDA problem: i) Domain shift. ii) Category shift. Towards tackling above challenges, we formulate a universal adaptation network called Dual Contrastive Network (DCN), where a contrastive module and a transferability rule are included. The experimental results reveal that DCN can work stably on different UniDA settings and exceeds the state-of-the-art performance across five benchmarks against existing DA methods.