Improving Open Set Domain Adaptation Using Image-to-Image Translation

The open set domain adaptation problem was rarely studied and its existing solutions are mostly based on learning a joint latent space which may encounter issues when the domains differ significantly from each other. This work is driven by the question whether or not it is beneficial to operate the source images to another image domain as close to the target as possible. We propose to address the open set domain adaptation problem by aligning sample at both feature space and pixel space. Our approach, called Open Set Translation and Adaptation Network (Ostan), consists of two main components: translation and adaptation. The translation model is a cycle-consistent generative adversarial network, which translates any source sample to the "style" of a target domain. The adaptation network is built upon OpenBP, an open set domain adaptation framework, and trained using both (labeled) translated source images and (unlabeled) target images. The proposed Ostan model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets. Our experiment also demonstrates that an image-to-image translation component can further improve the decision boundaries for both known and unknown classes.