A two-layer regression network for robust and accurate domain adaptation

Abstract

Naturally, classification models suffer from severe performance degradation when they are tested on datasets different from the ones used in training. Unsupervised domain adaptation helps to improve the generalizability of a pre-trained model by transferring knowledge from the labeled source domain (i.e., training dataset) to the unlabeled target domain (i.e., test dataset). The typical way of domain adaptation is to align the data distributions in embedding spaces between source and target domains. However, most existing works only enhance cross-domain consistency in the latent space disregarding the impact of source samples on the target classification task. Besides, discovering a subspace shared by two domains first and then training a transfer classifier separately hardly ensures that the obtained target features are suited for the classification model. To address these issues, in this work, we propose a two-layer regression network for domain adaptation (TRN-DA). TRN-DA learns class-wise domain invariant feature representations by jointly optimizing three tasks: supervised classification of source domain, unsupervised reconstruction of target domain, and alignment of cross-domain distributions. Furthermore, we introduce the concept of weight (importance) for source instances so that the resulting classifier can adapt well to the target domain. We formulate the domain adaptation problem as a unified optimization problem and solve it in an iterative way. Experimental results on different cross-domain image classification tasks demonstrate the encouraging performance of our TRN-DA compared to several recent state-of-the-art methods.