Unsupervised Domain Adaptation with Progressive Domain Augmentation

• URL: http://arxiv.org/abs/2004.01735v2
• Date: Fri, 24 Apr 2020 01:45:24 GMT
• Title: Unsupervised Domain Adaptation with Progressive Domain Augmentation
• Authors: Kevin Hua, Yuhong Guo
• Abstract summary: We propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain, progressively augments the source domain and bridges the source-target domain divergence. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.
• Score: 34.887690018011675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain. It is particularly challenging when there are significant divergences between the two domains. In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain interpolation, progressively augments the source domain and bridges the source-target domain divergence by conducting multiple subspace alignment on the Grassmann manifold. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.

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