Adversarial Transfer Learning for Cross-domain Visual Recognition

24 Nov 2017 Shan-Shan Wang Lei Zhang JingRu Fu

In many practical visual recognition scenarios, feature distribution in the source domain is generally different from that of the target domain, which results in the emergence of general cross-domain visual recognition problems. To address the problems of visual domain mismatch, we propose a novel semi-supervised adversarial transfer learning approach, which is called Coupled adversarial transfer Domain Adaptation (CatDA), for distribution alignment between two domains... (read more)

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