The creation of large-scale datasets annotated by humans inevitably introduces noisy
labels, leading to reduced generalization in deep-learning models. Sample selection-based …

In learning with noisy labels, the sample selection approach is very popular, which regards
small-loss data as correctly labeled during training. However, losses are generated on-the …

Learning with noisy labels is a vital topic for practical deep learning as models should be
robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning …

Deep models trained with noisy labels are prone to over-fitting and struggle in
generalization. Most existing solutions are based on an ideal assumption that the label …

A noisy training set usually leads to the degradation of the generalization and robustness of
neural networks. In this article, we propose a novel theoretically guaranteed clean sample …

The collection of large-scale datasets inevitably introduces noisy labels, leading to a
substantial degradation in the performance of deep neural networks (DNNs). Although …

Learning with noisy labels can significantly hinder the generalization performance of deep
neural networks (DNNs). Existing approaches address this issue through loss correction or …

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for
training deep neural networks (DNNs) robust to label noise have used two primary …

Label noise is ubiquitous in real-world scenarios, posing a practical challenge to supervised
models due to its effect in hurting the generalization performance of deep neural networks …

Learning with noisy labels aims to ensure model generalization given a label-corrupted
training set. The sample selection strategy achieves promising performance by selecting a …