Most recently proposed unsupervised domain adaptation algorithms attempt to learn domain invariant features by confusing a domain classifier through adversarial training. In this paper, we argue that this may not be an optimal solution in the real-world setting (a.k.a. in the wild) as the difference in terms of label information between domains has been largely ignored. As labeled instances are not available in the target domain in unsupervised domain adaptation tasks, it is difficult to explicitly capture the label difference between domains. To address this issue, we propose to learn a disentangled latent representation based on implicit autoencoders. In particular, a latent representation is disentangled into a global code and a local code. The global code is capturing category information via an encoder with a prior, and the local code is transferable across domains, which captures the “style” related information via an implicit decoder. Experimental results on digit recognition, object recognition and semantic segmentation demonstrate the effectiveness of our proposed method.

中文翻译：

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通过解开表示学习在野外进行无监督域适应

最近提出的无监督域自适应算法试图通过对抗性训练混淆域分类器来学习域不变特征。在本文中，我们认为这可能不是现实世界环境（也就是野外环境）中的最佳解决方案，因为在很大程度上忽略了域之间标签信息方面的差异。由于在无监督域适应任务中的目标域中没有标记实例，因此很难明确捕获域之间的标签差异。为了解决这个问题，我们建议学习基于隐式自动编码器的解缠结潜在表示。特别是，潜在表示被分解为全局代码和局部代码。全局代码通过具有先验的编码器捕获类别信息，并且本地代码可以跨域传输，它通过隐式解码器捕获“风格”相关信息。数字识别、对象识别和语义分割的实验结果证明了我们提出的方法的有效性。