Abstract
The training of accurate deep neural networks using noisy labels is attracting tremendous attention as of late. Several studies have been devoted to noisy-label-based learning using label correction methods. The latest label correction methods provide a theoretical guarantee and avoid the accumulation of irreversible errors. However, these methods often encounter class-biased problems: some classes (“easy”) are easier to correct than others (“hard”). It has been observed that the noisy samples with a lower class confidence are harder to correct, and the class confidence is measured by the average of the model predictions, resulting from the correctly predicted samples across all classes. To address this class-biased problem, we provide a theoretical analysis of the correction procedure by investigating model predictions. Furthermore, we propose a new balance label correction method by employing the statistics of contrastive loss as a dynamic balancing factor that focuses more on the lower confidence hard classes. To demonstrate the efficacy of the proposed method, we performed experiments on synthetic and real-world datasets with various noise patterns and levels. The experimental results demonstrated that the proposed method achieved a superior classification accuracy compared with other state-of-the-art noisy label learning frameworks.