Preserving Domain Private Representation via Mutual Information Maximization

Kavli Affiliate: Jing Wang

| First 5 Authors: Jiahong Chen, Jing Wang, Weipeng Lin, Kuangen Zhang, Clarence W. de Silva

| Summary:

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.

| Search Query: ArXiv Query: search_query=au:”Jing Wang”&id_list=&start=0&max_results=10

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