  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.
