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While much of recent study in semi-supervised learning (SSL) has achievedstrong performance on single-label classification problems, an equallyimportant yet underexplored problem is how to leverage the advantage ofunlabeled data in multi-label classification tasks. To extend the success ofSSL to multi-label classification, we first analyze with illustrative examplesto get some intuition about the extra challenges exist in multi-labelclassification. Based on the analysis, we then propose PercentMatch, apercentile-based threshold adjusting scheme, to dynamically alter the scorethresholds of positive and negative pseudo-labels for each class during thetraining, as well as dynamic unlabeled loss weights that further reduces noisefrom early-stage unlabeled predictions. Without loss of simplicity, we achievestrong performance on Pascal VOC2007 and MS-COCO datasets when compared torecent SSL methods.

PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification