Transferable deep representations for visual domain adaptation (DA) provides a route to learn from labeled source images to recognize target images without the aid of target-domain supervision. Relevant researches increasingly arouse a great amount of interest due to its potential industrial prospect for non-laborious annotation and remarkable generalization. However, DA presumes source images are identically sampled from a single source while Multi-Source DA (MSDA) is ubiquitous in the real-world. In MSDA, the domain shifts exist not only between source and target domains but also among the sources; especially, the multi-source and target domains may disagree on their semantics (e.g., category shifts). This issue challenges the existing solutions for MSDAs. In this paper, we propose Deep CockTail Network (DCTN), a universal and flexibly-deployed framework to address the problems. DCTN uses a multi-way adversarial learning pipeline to minimize the domain discrepancy between the target and each of the multiple in order to learn domain-invariant features. The derived source-specific perplexity scores measure how similar each target feature appears as a feature from one of source domains. The multi-source category classifiers are integrated with the perplexity scores to categorize target images. We accordingly derive a theoretical analysis towards DCTN, including the interpretation why DCTN can be successful without precisely crafting the source-specific hyper-parameters, and target expected loss upper bounds in terms of domain and category shifts. In our experiments, DCTNs have been evaluated on four benchmarks, whose empirical studies involve vanilla and three challenging category-shift transfer problems in MSDA, i.e., source-shift, target-shift and source-target-shift scenarios. The results thoroughly reveal that DCTN significantly boosts classification accuracies in MSDA and performs extraordinarily to resist negative transfers across different MSDA scenarios.

用于视觉域自适应（DA）的可传递深度表示提供了一种从标记的源图像中学习的方法，从而无需目标域监督即可识别目标图像。由于其潜在的非劳动注释和非凡的概括性的工业前景，相关的研究越来越引起人们的兴趣。但是，DA假定源图像是从单个源中相同采样的，而多源DA（MSDA）在现实世界中无处不在。在MSDA中，域转移不仅存在于源域和目标域之间，而且还存在于源域之间。特别是，多源域和目标域可能在语义上存在分歧（例如类别转移）。此问题对现有的MSDA解决方案提出了挑战。在本文中，我们提出了Deep CockTail网络（DCTN），以解决这些问题。为了学习领域不变特征，DCTN使用多方对抗学习管道来最小化目标与多个目标之间的领域差异。导出的特定于源的困惑度分数衡量每个目标特征作为源域之一中的特征出现的相似程度。多源类别分类器与困惑度分数集成在一起，以对目标图像进行分类。因此，我们对DCTN进行了理论分析，包括以下解释：为什么在不精确设计特定于源的超参数的情况下DCTN能够成功，并针对域和类别偏移指定了预期的损失上限。在我们的实验中，DCTN已在四个基准上进行了评估，其经验研究涉及香草和MSDA中的三个具有挑战性的类别转移转移问题，即源转移，目标转移和源-目标转移方案。结果彻底表明，DCTN显着提高了MSDA中的分类准确性，并且在抵抗不同MSDA方案之间的负迁移方面表现出色。