APIMatch: A Semi-Supervised Learning Framework with Average Pseudo Integrated Margin

Résumé fourni par la source

Semi-supervised learning (SSL) is an effective approach to leverage limited labeled data alongside abundant unlabeled data. While methods like MarginMatch have shown promise by using the average pseudo margin (APM) to evaluate pseudo-label reliability, they face two limitations: (1) APM struggles to distinguish correctly and incorrectly pseudo-labeled classes for hard-to-learn samples, and (2) fixed-percentile thresholding leads to suboptimal pseudo-label utilization throughout training. To address these challenges, we propose APIMatch, a novel SSL framework introducing the average pseudo integrated margin (APIM) metric and a negative-sample-aware dynamic percentile thresholding strategy. The core novelty lies in jointly modeling the pseudo-labeled class and competitive non-pseudo-labeled classes to accurately re-evaluate hard-to-learn samples, coupled with a confidence-distribution-aware adaptive threshold. Specifically, APIM jointly considers the logit differences between the pseudo-labeled class and both the largest and second-largest non-pseudo-labeled classes, enabling more accurate confidence evaluation for hard-to-learn samples. In addition, our dynamic thresholding strategy constructs a pseudo-negative-sample reference set to model confidence distributions and adaptively adjusts the percentile threshold based on training progression, improving both recall in early stages and precision in later stages. Extensive experiments on CIFAR-10, CIFAR-100, and STL-10 demonstrate that APIMatch achieves competitive performance against state-of-the-art methods, particularly in extremely low-label scenarios. Under the 1 label per class setting, APIMatch achieves error rates of 1.84 % and 36.7 % on CIFAR-10 and CIFAR-100, respectively. On STL-10 with only 4 labels per class, APIMatch achieves an error rate of 18.47 % ± 0.65 % , representing a 6.9 % absolute accuracy improvement over MarginMatch, demonstrating strong robustness under severe label scarcity.

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