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arXiv · 2106.11915

Enhanced Separable Disentanglement for Unsupervised Domain Adaptation

Youshan Zhang·Brian D. Davison
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Abstract

Domain adaptation aims to mitigate the domain gap when transferring knowledge from an existing labeled domain to a new domain. However, existing disentanglement-based methods do not fully consider separation between domain-invariant and domain-specific features, which means the domain-invariant features are not discriminative. The reconstructed features are also not sufficiently used during training. In this paper, we propose a novel enhanced separable disentanglement (ESD) model. We first employ a disentangler to distill domain-invariant and domain-specific features. Then, we apply feature separation enhancement processes to minimize contamination between domain-invariant and domain-specific features. Finally, our model reconstructs complete feature vectors, which are used for further disentanglement during the training phase. Extensive experiments from three benchmark datasets outperform state-of-the-art methods, especially on challenging cross-domain tasks.

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Youshan Zhang, Brian D. Davison. 2021-06-22. Enhanced Separable Disentanglement for Unsupervised Domain Adaptation. https://arxiv.org/abs/2106.11915

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