# PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification Source: [PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:30:35.914Z Authors: Junxiang Huang, Alexander Huang, Beatriz C. Guerra, Yen-Yun Yu Published: 2022-08-30T01:27:48.000Z ## Abstract While much of recent study in semi-supervised learning (SSL) has achieved strong performance on single-label classification problems, an equally important yet underexplored problem is how to leverage the advantage of unlabeled data in multi-label classification tasks. To extend the success of SSL to multi-label classification, we first analyze with illustrative examples to get some intuition about the extra challenges exist in multi-label classification. Based on the analysis, we then propose PercentMatch, a percentile-based threshold adjusting scheme, to dynamically alter the score thresholds of positive and negative pseudo-labels for each class during the training, as well as dynamic unlabeled loss weights that further reduces noise from early-stage unlabeled predictions. Without loss of simplicity, we achieve strong performance on Pascal VOC2007 and MS-COCO datasets when compared to recent SSL methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()