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.

本文提出了基于生成对抗网络的多源域自适应方法，通过将图像特征投影到保留内容依赖性的空间，再将这个不变表示重新投影到像素空间，从而生成训练目标分类器所需的新标记图像。通过 MSDA 基准测试，表明本文的方法优于现有技术。