Enhanced Separable Disentanglement for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2106.11915v1
• Date: Tue, 22 Jun 2021 16:50:53 GMT
• Title: Enhanced Separable Disentanglement for Unsupervised Domain Adaptation
• Authors: Youshan Zhang and Brian D. Davison
• Abstract summary: Domain adaptation aims to mitigate the domain gap when transferring knowledge from an existing labeled domain to a new domain. Existing disentanglement-based methods do not fully consider separation between domain-invariant and domain-specific features. In this paper, we propose a novel enhanced separable disentanglement model.
• Score: 6.942003070153651
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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