Sample selection is the most straightforward technique to combat label noise, aiming to
distinguish mislabeled samples during training and avoid the degradation of the robustness …

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 …

Noisy labels damage the performance of deep networks. For robust learning, a prominent
two-stage pipeline alternates between eliminating possible incorrect labels and semi …

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 …

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

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 …

Acquiring accurate labels on large-scale datasets is both time consuming and expensive. To
reduce the dependency of deep learning models on learning from clean labeled data …

Deep neural networks and large-scale datasets have revolutionized the field of machine
learning. However, these large networks are susceptible to overfitting to label noise …

The presence of noisy labels in a training dataset can significantly impact the performance of
machine learning models. To tackle this issue, researchers have explored methods for …

Learning with noisy labels is nontrivial for deep learning models. Sample selection is a
widely investigated research topic for handling noisy labels. However, most existing …