Handling Class Imbalance in Black-Box Unsupervised Domain Adaptation with Synthetic Minority Over-Sampling

Black-box unsupervised domain adaptation (BBUDA) is a challenging task that transfers knowledge from the source domain to the target domain without access to the source data and source model, thus alleviating public concerns about data security. However, BBUDA requires the source model to function as a black-box predictor for the target data, and the pseudo-labels often exhibit class imbalance, which degrades the performance. To tackle this problem, we propose employing the synthetic minority oversampling technique (SMOTE) and adaptive sampling to rebalance data. Given that predictions often contain errors, we first select reliable high-confidence data before using SMOTE to generate synthetic samples for the minority class. Second, we incrementally select high-confidence data from the remaining low-confidence data with an adaptive sampling rate for each class, in which the minority class (with the fewest samples) is assigned a higher sampling rate and the majority class (with the most samples) is assigned a lower sampling rate. The experimental results demonstrate that our method can mitigate the class imbalance and further improve the performance of the target model.