Self-Alignment for Black-Box Domain Adaptation of Image Classification

Abstract

Recently, black-box domain adaptation attracts a lot of attention, which is a new concept to realize domain adaptation with only a cloud API service instead of the source data or well-trained source model, reflecting the focus on development of cloud services and concerns about data security. However, the existing black-box domain adaptation methods always only use high-confidence samples which limits their performance. We propose a self-alignment approach based on statistic moment matching to realize black-box domain adaptation. We construct a model for target domain in the initial stage of our work. Then, we put target data into source model API to obtain the pseudo-labels and divide the target data into high-confidence and low-confidence parts according to their pseudo-labels confidence. By matching the data distributions between these two parts and self-supervised learning on high-confidence part, the performance on both parts samples can be boosted respectively. Information maximization is also applied to the target data to further improve their classification performance. Experiment results confirm that our method achieves state-of-the-art performance.