Abstract. Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for closed-set scenario, where the source and the target domain completely share the class of their samples. We call the shared class the "known class." However, in practice, when samples in target domain are not labeled, we cannot know whether the domains share the class. A target domain can contain samples of classes that are not shared by the source domain. We call such classes the "unknown class" and algorithms that work well in the open set situation are very practical. However, most existing distribution matching methods for domain adaptation do not work well in this setting because unknown target samples should not be aligned with the source. In this paper, we propose a method for an open set domain adaptation scenario which utilizes adversarial training. A classifier is trained to make a boundary between the source and the target samples whereas a generator is trained to make target samples far from the boundary. Thus, we assign two options to the feature generator: aligning them with source known samples or rejecting them as unknown target samples. This approach allows to extract features that separate unknown target samples from known target samples. Our method was extensively evaluated on domain adaptation setting and outperformed other methods with a large margin in most setting.
The aforementioned methods tackle with domain adaptation in the closed- where the unknown class does not exist in the source domain is raised by Saito et al. REF .
Open Set Domain Adaptation by Backpropagation
ArXiv
ArXiv
Computer Science