Pseudo labeling is a popular and effective method to leverage the information
of unlabeled data. Conventional instance-aware pseudo labeling methods often
assign each unlabeled instance with a pseudo label based on its predicted
probabilities. However, due to the unknown number of true labels, these methods
cannot generalize well to semi-supervised multi-label learning (SSMLL)
scenarios, since they would suffer from the risk of either introducing false
positive labels or neglecting true positive ones. In this paper, we propose to
solve the SSMLL problems by performing Class-distribution-Aware Pseudo labeling
(CAP), which encourages the class distribution of pseudo labels to approximate
the true one. Specifically, we design a regularized learning framework
consisting of the class-aware thresholds to control the number of pseudo labels
for each class. Given that the labeled and unlabeled examples are sampled
according to the same distribution, we determine the thresholds by exploiting
the empirical class distribution, which can be treated as a tight approximation
to the true one. Theoretically, we show that the generalization performance of
the proposed method is dependent on the pseudo labeling error, which can be
significantly reduced by the CAP strategy. Extensive experimental results on
multiple benchmark datasets validate that CAP can effectively solve the SSMLL
problems.

本文提出 Class-distribution-Aware Pseudo Labeling (CAP) 方法，通过设计基于类感知阈值的规则学习框架来控制每个类别的伪标签的数量，并约束伪标签的类别分布接近真实分布，旨在解决半监督多标签学习问题。实验结果证明了 CAP 方法可以有效解决半监督多标签学习问题并提高泛化能力。