Preserving Domain Private Representation via Mutual Information Maximization
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Abstract (arXiv)
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the generalization of a model to an unlabeled data domain. Nevertheless, the existing methods fail to effectively preserve the representation that is private to the label-missing domain, which could adversely affect the generalization. In this paper, we propose an approach to preserve such representation so that the latent distribution of the unlabeled domain could represent both the domain-invariant features and the individual characteristics that are private to the unlabeled domain. In particular, we demonstrate that maximizing the mutual information between the unlabeled domain and its latent space while mitigating the domain divergence can achieve such preservation. We also theoretically and empirically validate that preserving the representation that is private to the unlabeled domain is important and of necessity for the cross-domain generalization. Our approach outperforms state-of-the-art methods on several public datasets.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 9 Jan 2022.
Builds on
21 of 45 references.
References · 21
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9 more
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Citing papers per year: 2022: 3.
3 papers.