AdapMatch: Adaptive Bias Decoupling for Semi-Supervised Partial Label Learning under Unknown Class Distributions

Partial Label Learning (PLL) trains models from instances with candidate label sets containing the ground-truth, while Semi-Supervised Partial Label Learning (SSPLL) further leverages the available unlabeled data to prompt the performance. The previous SSPLL methods implicitly assume the unlabeled data are class-balanced distributed. However, in realistic scenarios, the class distribution of unlabeled data is unknown, resulting in performance collapse on minority classes for the existing SSPLL methods. We identify two coupled biases behind this failure: (i) pseudo-label selection bias, where high-confidence filtering tends to over-select samples from dominant classes, causing the training data to become increasingly skewed toward these classes; and (ii) disambiguation bias, where dominant classes are more likely to win within candidate sets, even when they are incorrect. Theoretically, we show that these two biases reinforce each other over training, forming a feedback loop that degrades generalization performance. To weaken this loop, we propose AdapMatch, which uses DAS to reduce the skew in training data caused by pseudo-label selection bias via prior-aware pseudo-label admission and CAD to lower the disambiguation error by suppressing candidate-set domination. Across five benchmarks, AdapMatch yields gains up to 41.31\% at most , showing strong robustness to varying scale, imbalance, and ambiguity. The code is available at \url{https://anonymous.4open.science/r/AdapMatch-8BC5}.