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

• URL: http://arxiv.org/abs/2208.13946v1
• Date: Tue, 30 Aug 2022 01:27:48 GMT
• Title: PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification
• Authors: Junxiang Huang, Alexander Huang, Beatriz C. Guerra, Yen-Yun Yu
• Abstract summary: We propose a percentile-based threshold adjusting scheme to dynamically alter the score thresholds of positive and negative pseudo-labels for each class during the training. We achieve strong performance on Pascal VOC2007 and MS-COCO datasets when compared to recent SSL methods.
• Score: 64.39761523935613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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