Multi-labeled complementary label learning (MLCLL) is a resource-efficient paradigm aimed at reducing labeling efforts in multi-label learning (MLL). While existing methods address the MLCLL problem using neural network-based models, they often overfit to noisy information, leading to sharp decision boundaries. This overfitting issue is further exacerbated when the label correlation, which could help denoise the supervision, is not fully explored in existing works. In this paper, we propose a novel framework called NMCB to alleviate the impact of noisy information in MLCLL, which makes a first attempt to explore mixup for MLCLL problem. Specifically, a tailored version of mixup is employed to achieve a smoother decision boundary of the trained classifier, thereby reducing the sensitivity of NMCB to noisy labels and enhancing its generalization ability. Moreover, NMCB applies a model to automatically extract label correlations from non-complementary labels transformed by mixup during the learning process. These extracted correlations serve as alignment objectives for the output distribution of instance augmentations within a consistency regularization term of NMCB, further improving the model performance. Empirical studies demonstrate the effectiveness of the proposed method.

Multiple complementary-label learning (MCLL) is a machine learning task that involves learning a classifier from instances with multiple complementary labels (MCLs). MCLs are labels that indicate the incorrect labels of an instance. Previous methods for learning with ambiguous supervised information may not be effective because MCLs only make up a small proportion of all labels. In this article, we propose MulCo, a simple yet effective framework that uses contrastive learning to enhance the representation capability in MCLL. Contrastive learning involves contrasting semantically similar and dissimilar pairs of instances, with the goal of benefiting from negatives whose ground-truth labels differ from those of anchors. However, it is possible for dissimilar pairs to have the same label due to the random sampling of negatives from inaccurately labeled data. To solve this problem, we design a sifted contrastive loss for MulCo to correct the sampling of same-label negative pairs. We also provide theoretical evidence for the feasibility of the sifted contrastive loss by establishing an upper bound on the ideal contrastive loss. Correspondingly, we develop two progressive solutions using the properties of complementary labels to approximate the ideal contrastive loss through weighting. Our empirical study demonstrates the effectiveness of the proposed method. The code of this article is available at https://github.com/gaoyi439/MulCo .