Open Set Domain Adaptation with Multi-Classifier Adversarial Network

Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. The majority of existing domain adaptation methods rely on the assumption of having identical label spaces across the source and target domains, which limits their application in real-world scenarios. To get rid of such an assumption, prior research has introduced various open set domain adaptation settings in the literature. This paper focuses on the type of open set domain adaptation setting where the target domain has both private (`unknown classes') label space beside the shared (`known classes') label space. However, the source domain only has the `known classes' label space. Prevalent distribution-matching domain adaptation methods are inadequate in such a setting that demands adaptation from a smaller source domain to a larger and diverse target domain with more classes. For addressing this specific open set domain adaptation setting, prior research introduces a domain adversarial model with an empirical fixed threshold which lacks at handling false-negative transfers. We propose a multi-classifier based weighting scheme for the adversarial domain adaptation model to address this issue and improve performance. Our proposed method assigns distinguishable weights to target samples belonging to the known and unknown classes to limit false-negative transfers, and simultaneously reduce the domain gap between shared classes of the source and target domains. A thorough evaluation shows that our proposed method outperforms existing domain adaptation methods for a number of domain adaptation datasets.