Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks. Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content. For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style. In this way, new labeled images can be generated which are used to train a final target classifier. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.
@article{arxiv.2004.08769,
title = {TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation},
author = {Subhankar Roy and Aliaksandr Siarohin and Enver Sangineto and Nicu Sebe and Elisa Ricci},
journal= {arXiv preprint arXiv:2004.08769},
year = {2021}
}