CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning

Shiyu Tian1; Hongxin Wei2; Yiqun Wang1; Lei Feng3

10.1109/CVPR52733.2024.01842

2024-06-22

1063-6919

979-8-3503-5301-3

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

16-22 June 2024

Seattle, WA, USA

Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have been widely explored to tackle label ambiguity issues in PLL, which regard the true label as a latent variable to be identified. However, identifying the true labels accurately and completely remains challenging, causing noise in pseudo labels during model training. In this paper, we propose a new method called CroSel, which leverages historical predictions from the model to identify true labels for most training examples. First, we introduce a cross selection strategy, which enables two deep models to select true labels of partially labeled data for each other. Besides, we propose a novel consistency regularization term called comix to avoid sample waste and tiny noise caused by false selection. In this way, CroSel can pick out the true labels of most examples with high precision. Extensive experiments demonstrate the superiority of CroSel, which consistently outperforms previous state-of-the-art methods on benchmark datasets. Additionally, our method achieves over 90% accuracy and quantity for selecting true labels on CIFAR-type datasets under various settings.

其他

1.Chongqing University
2.Southern University of Science and Technology
3.Singapore University of Technology and Design

Shiyu Tian,Hongxin Wei,Yiqun Wang,et al. CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning[C],2024.