TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation

• URL: http://arxiv.org/abs/2004.08769v1
• Date: Sun, 19 Apr 2020 05:07:22 GMT
• Title: TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
• Authors: Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe, Elisa Ricci
• Abstract summary: 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 and the content. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.
• Score: 82.52514546441247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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