Data Augmentation With Regularization for Multi-Labeled Complementary Label Learning.

Abstract

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.