Learning discriminative feature via a generic auxiliary distribution for unsupervised domain adaptation

Traditional methods for unsupervised domain adaptation often leverage a projection matrix or a neural network as the feature extractor or classifier, where the feature extractor shared by the source and target domains enables the sample distributions to be aligned in the feature space, and simultaneously makes the source domain features separability enough for the classifier. However, only the alignment of both domains is not enough because the inter-class distance of some categories in the target domain may be too small, i.e., the feature separability is poor, which often leads to the bad condition that some samples are projected to the classification boundaries and thus misclassified. To solve this problem, we propose a pluggable generic auxiliary distribution (GAD) module for target domain in this paper. The proposed GAD module can iteratively refine the prediction of the target domain samples to increase the separability of the learned features, thereby increasing the distance between features of different categories. This operation can finally reduce the possibility of the target domain samples falling near the classification boundary, and leads to the improvement of classification accuracy for the target domain. Extensive experiments on several popular datasets are conducted, and the results demonstrate the effectiveness of the proposed method.