论文标题
百分比：基于百分比的多标签半监督分类的动态阈值
PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification
论文作者
论文摘要
尽管半监督学习（SSL）的最新研究已经在单标签分类问题上取得了强劲的表现，但同样重要但毫无疑问的问题是如何利用多标签分类任务中未标记数据的优势。为了将SSL的成功扩展到多标签分类，我们首先用说明性示例分析了有关多标签分类中存在的额外挑战的直觉。基于分析，我们提出了一个基于百分位的阈值调整方案的百分位匹配，以动态地改变训练过程中每个类别的正值和负伪标签的得分阈值，以及动态的未标记失误权重，从而进一步降低了早期未标记的预测，从而进一步降低了噪声。与最近的SSL方法相比，在没有简单性的情况下，我们在Pascal VOC2007和MS-Coco数据集上实现了强劲的性能。
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.
