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Heterogeneous domain adaptation (HDA) tackles the learning of cross-domainsamples with both different probability distributions and featurerepresentations. Most of the existing HDA studies focus on the single-sourcescenario. In reality, however, it is not uncommon to obtain samples frommultiple heterogeneous domains. In this article, we study the multisource HDAproblem and propose a conditional weighting adversarial network (CWAN) toaddress it. The proposed CWAN adversarially learns a feature transformer, alabel classifier, and a domain discriminator. To quantify the importance ofdifferent source domains, CWAN introduces a sophisticated conditional weightingscheme to calculate the weights of the source domains according to theconditional distribution divergence between the source and target domains.Different from existing weighting schemes, the proposed conditional weightingscheme not only weights the source domains but also implicitly aligns theconditional distributions during the optimization process. Experimental resultsclearly demonstrate that the proposed CWAN performs much better than severalstate-of-the-art methods on four real-world datasets.

Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network