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 source 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.

大多数域适应方法都考虑了将知识从单源数据集传输到目标域的问题。但是，在实际应用中，我们通常可以访问多个资源。在本文中，我们提出了基于生成对抗网络的第一种多源域自适应（MSDA）方法。我们的方法受到观察的启发，即给定图像的外观取决于三个因素：领域，样式（以低级特征变化为特征）和内容。因此，我们建议将源图像特征投影到仅保留来自内容的依赖性的空间上，然后使用目标域和样式将此不变表示形式重新投影到像素空间上。以这种方式，可以生成新的标记图像，这些图像用于训练最终目标分类器。我们使用通用的MSDA基准测试了我们的方法，表明其性能优于最新方法。