Deep joint subdomain alignment for unsupervised domain adaptation

Abstract

Domain adaptation (DA) aims to transfer knowledge of the labeled source domain to the unlabeled target domain. Current DA methods learn domain-invariant features by aligning source and target feature spaces by distribution discrepancy minimization or adversarial training. However, existing DA methods fail to consider the semantic-level alignment, which can lead to the distortion of semantic feature structures for the target tasks. In this paper, we propose a novel domain adaptation method, called Deep Joint Subdomain Alignment (DJSA), which jointly conducts intra-subdomain and inter-subdomain alignment for fine-grained classification of target tasks. DJSA obtains the pseudo labels of unlabeled target samples by measuring the similarity between the target samples and source category centers, and then DJSA calculates an intra-subdomain loss to reduce the difference within the same class and increase the difference between different classes in both the source domain and the target domain, and DJSA utilizes a Local Maximum Mean Discrepancy (LMMD) metric to measure the inter-subdomain discrepancy between the source and target domain, and minimizes the discrepancy to align different domains. Furthermore, extensive experimental results demonstrate the effectiveness and superiority of our proposed method in unsupervised domain adaptation.