Unified Deep Supervised Domain Adaptation and Generalization

Abstract

This work addresses the problem of domain adaptation and generalization in a unified fashion. The main idea is to exploit the siamese architecture with the Contrastive Loss to address the domain shift and generalization problems. The framework is general, and can be used with any architecture. One of the main strengths of the approach is the "speed" of adaptation, which requires an extremely low number of labeled training samples from the target domain, even only one per category. The same architecture and loss function can be easily extended to domain generalization. We present state-of-the-art results for both of these applications.