Semantic Dual-Adversarial Network for Blended-Target Domain Adaptation

The popularity of blended-target domain adaptation (BTDA) is growing since target data in the real world often come from multiple domains with different data distributions. Most BTDA studies adapt directly from the source domain to the target domains without considering which kinds of semantic information embedded in images should be explored. Therefore, some irrelevant semantic information is inevitably used, which leads to negative transfer. To address these issues, we propose a semantic dual-adversarial network (SDN) method for BTDA. Specifically, to suppress irrelevant semantic information, we adopt a min-max game strategy between the classifier and the feature extractor. The classifier tries to maximize the prediction distribution discrepancy, whereas the extractor endeavors to minimize this discrepancy. In this process, irrelevant semantic information is suppressed and the principal semantic information is emphasized. To align the categorical distributions, we train a category-aware domain discriminator and a feature extractor with category labels. In addition, we introduce a random ratio-based feature fusion scheme to augment the source domain, which can decrease domain gaps. At last, we propose a weighted negative self-supervised learning method to enhance the model's generalization. Extensive experiments on multiple benchmarks showcase that our method significantly outperforms the prior state-of-the-art methods in BTDA.