Teacher-Student Competition for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2010.09572v2
• Date: Tue, 20 Oct 2020 03:37:22 GMT
• Title: Teacher-Student Competition for Unsupervised Domain Adaptation
• Authors: Ruixin Xiao, Zhilei Liu, Baoyuan Wu
• Abstract summary: This paper proposes an unsupervised domain adaptation approach with Teacher-Student Competition (TSC) In particular, a student network is introduced to learn the target-specific feature space, and we design a novel competition mechanism to select more credible pseudo-labels for the training of student network. Our proposed TSC framework significantly outperforms the state-of-the-art domain adaptation methods on Office-31 and ImageCLEF-DA benchmarks.
• Score: 28.734814582911845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared feature extractor, which causes the source-bias problem. This paper proposes an unsupervised domain adaptation approach with Teacher-Student Competition (TSC). In particular, a student network is introduced to learn the target-specific feature space, and we design a novel competition mechanism to select more credible pseudo-labels for the training of student network. We introduce a teacher network with the structure of existing conventional UDA method, and both teacher and student networks compete to provide target pseudo-labels to constrain every target sample's training in student network. Extensive experiments demonstrate that our proposed TSC framework significantly outperforms the state-of-the-art domain adaptation methods on Office-31 and ImageCLEF-DA benchmarks.

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