Learning to Adapt to Label-Scarce Image Domain via Angular Distance-Based Feature Alignment

Abstract

Most recent domain adaptation (DA) methods deal with unsupervised setup, which requires numerous target images for training. However, constructing a large-scale image set of the target domain is occasionally much harder than preparing a smaller number of image and label pairs. To cope with the problem, a great attention is recently paid to supervised domain adaptation (SDA), which takes an extremely small amount of labeled target images for training (e.g., at most three examples per category). In the SDA setup, adapting deep networks towards target domain is very challenging due to the lack of target data, and we tackle this problem as follows. Given labeled images from source and target domains, we first extract deep features and project them to hyper-spherical space via l2-normalization. Afterwards, an additive angular margin loss is embedded so that deep features of both domains are compactly grouped on the basis of shared class prototypes. To further relieve domain discrepancy, a pairwise spherical feature alignment loss is incorporated. All of our loss functions are defined in the hyper-spherical space, and the advantage of each ingredient is analyzed in the literature. Comparative evaluation results demonstrate that the proposed approach is superior to existing SDA methods, achieving 60.7% (1-shot) and 64.4% (3-shot) average accuracies for the DomainNet benchmark dataset using the ResNet-34 backbone. In addition, by applying a semi-supervised learning scheme to a network initialized by our SDA method, we achieve the state-of-the-art performance on semi-supervised domain adaptation (SSDA) as well.