Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These limitations often result in the introduction of false positive labels or the neglect of true positive ones. To overcome these challenges, this paper proposes a novel solution called Class-distribution-Aware Pseudo-labeling (CAP) that performs pseudo-labeling in a class-aware manner. The proposed approach introduces a regularized learning framework incorporating class-aware thresholds, which effectively control the assignment of positive and negative pseudo-labels for each class. Notably, even with a small proportion of labeled examples, our observations demonstrate that the estimated class distribution serves as a reliable approximation. Motivated by this finding, we develop a class-distribution-aware thresholding (CAT) strategy to ensure the alignment of pseudo-label distribution with the true distribution. Moreover, we extend CAT into a label decision method, aiming to improve the model's classification performance during the testing phase. The correctness of the estimated class distribution is theoretically verified, and a generalization error bound is provided for our proposed method. Extensive experiments on multiple benchmark datasets confirm the efficacy of CAP in addressing the challenges of SSMLL problems.

The goal of semi-supervised multi-label learning (SSMLL) is to improve model performance by leveraging the information of unlabeled data. Recent studies usually adopt the pseudo-labeling strategy to tackle unlabeled data based on the assumption that labeled and unlabeled data share the same distribution. However, in realistic scenarios, unlabeled examples are often collected through cost-effective methods, inevitably introducing out-of-distribution (OOD) data, leading to a significant decline in model performance. In this paper, we propose a safe semi-supervised multi-label learning framework based on the theory of evidential deep learning (EDL), with the goal of achieving robust and effective unlabeled data exploitation. On one hand, we propose the asymmetric beta loss to not only compensate for the lack of robustness in common MLL losses, but also to solve the inherent positive-negative imbalance problem faced by the EDL losses in MLL. On the other hand, to construct a robust SSMLL framework, we adopt a dual-head structure to generate class probabilities and instance uncertainties. The former are used to generate pseudo-labels, while the latter are utilized to filter OOD examples. To avoid the need for threshold estimation, we develop a dual-measurement weighted loss function to safely perform unlabeled training. Extensive experiments on multiple benchmark datasets verify the effectiveness of the proposed method in both OOD detection and SSMLL tasks.

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These limitations often result in the introduction of false positive labels or the neglect of true positive ones. To overcome these challenges, this paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner. The proposed approach introduces a regularized learning framework incorporating class-aware thresholds, which effectively control the assignment of positive and negative pseudo-labels for each class. Notably, even with a small proportion of labeled examples, our observations demonstrate that the estimated class distribution serves as a reliable approximation. Motivated by this finding, we develop a class-distribution-aware thresholding strategy to ensure the alignment of pseudo-label distribution with the true distribution. The correctness of the estimated class distribution is theoretically verified, and a generalization error bound is provided for our proposed method. Extensive experiments on multiple benchmark datasets confirm the efficacy of CAP in addressing the challenges of SSMLL problems.