Adaptative Balanced Distribution for Domain Adaptation With Strong Alignment

Aligning and balancing the marginal and conditional feature distributions are two critical procedures for unsupervised domain adaptation (UDA) problems. However, existing methods usually consider the former while ignoring the latter. To improve the problems of instability and imbalance, we propose the Adaptative Joint Distribution Adaptation Network (AJDAN) by analyzing the multi-modal interactions between the two types of distributions and adding a self-learning network to simultaneously balance them. Furthermore, we give higher weights to samples that are far away from the domain boundary (easy-to-classify samples) using Strong Binary Cross-Entropy (SBCE). The strong alignment strategy is adjustable and allows the network to better train easy-to-classify samples than traditional Binary Cross-Entropy (BCE) in various scenarios. The experiment shows that AJDAN with SBCE (AJDAN+S) provides an average of 68.3% accuracy on the Office-Home dataset, and 89.1% accuracy on the Office31 dataset, showing its superiority by 2~3 percent above the existing state-of-the-art methods.