Alleviating Confirmation Bias in Learning with Noisy Labels via Two-Network Collaboration

Deep neural networks (DNNs) have achieved remarkable success in various computer vision tasks, e.g., image classification. However, most of the existing models depend heavily on annotated data, where label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of DNNs. To this end, recent advances in learning with noisy labels (LNL) adopt the sample selection strategy that identifies clean samples from the noisy dataset to update DNNs, using semi-supervised learning where rejected samples are treated as unlabeled data. However, existing LNL methods often overlook the varying fitting difficulties of different classes, resulting in suboptimal sample selection and confirmation bias, and consequently, the errors accumulate during semi-supervised training. In this article, we propose a novel method, TNCollab, which aims at alleviating confirmation bias in both sample selection and semi-supervised training stages via two-network collaboration. Specifically, we introduce a class-adaptive threshold for sample selection to address the varying fitting difficulties across different classes. Additionally, we construct a hard set consisting of samples where the two networks disagree and introduce a noise-robust loss to extract potentially useful information while maintaining robustness against label noise. Furthermore, we propose a dual consistency loss to ensure consistent predictions between the networks across different augmented views of the same sample, facilitating mutual learning. Extensive experiments demonstrate that TNCollab achieves state-of-the-art performance on image classification and facial expression recognition tasks, particularly on CIFAR-10, CIFAR-100, WebVision, Clothing1M, Tiny-ImageNet, and RAF-DB datasets, showing improved visual understanding and generalization capabilities. Our codes are available at https://github.com/Delete12137/TNCollab .