Open-set domain adaptation by deconfounding domain gaps

Abstract

Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps (DDP) during class separation and domain adaptation in OSDA. The mechanism of DDP is to transfer domain-related styles and contexts from the target domain to the source domain. It enables the model to recognize a class as known (or unknown) because of the class semantics rather than the confusion caused by spurious styles or contexts. In addition, we propose a module of ensembling multiple transformations (EMT) to produce calibrated recognition scores, i.e., reliable normality scores, for the samples in the target domain. Extensive experiments on two standard benchmarks verify that our proposed method outperforms a wide range of OSDA methods, because of its advanced ability of correctly recognizing unknown classes.