Dual Adversarial Domain Adaptation

• URL: http://arxiv.org/abs/2001.00153v1
• Date: Wed, 1 Jan 2020 07:10:09 GMT
• Title: Dual Adversarial Domain Adaptation
• Authors: Yuntao Du, Zhiwen Tan, Qian Chen, Xiaowen Zhang, Yirong Yao, Chongjun Wang
• Abstract summary: Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Recent experiments have shown that when the discriminator is provided with domain information in both domains, it is able to preserve the complex multimodal information. We adopt a discriminator with $2K$-dimensional output to perform both domain-level and class-level alignments simultaneously in a single discriminator.
• Score: 6.69797982848003
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous adversarial domain adaptation methods mostly adopt the discriminator with binary or $K$-dimensional output to perform marginal or conditional alignment independently. Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains. Following this idea, we adopt a discriminator with $2K$-dimensional output to perform both domain-level and class-level alignments simultaneously in a single discriminator. However, a single discriminator can not capture all the useful information across domains and the relationships between the examples and the decision boundary are rarely explored before. Inspired by multi-view learning and latest advances in domain adaptation, besides the adversarial process between the discriminator and the feature extractor, we also design a novel mechanism to make two discriminators pit against each other, so that they can provide diverse information for each other and avoid generating target features outside the support of the source domain. To the best of our knowledge, it is the first time to explore a dual adversarial strategy in domain adaptation. Moreover, we also use the semi-supervised learning regularization to make the representations more discriminative. Comprehensive experiments on two real-world datasets verify that our method outperforms several state-of-the-art domain adaptation methods.

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