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 …

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 …

Existing deep learning methods often require a large amount of high-quality labeled data.
Yet, the presence of noisy labels in the real-world training data seriously affects the …

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 …

The sample selection approach is popular in learning with noisy labels. The state-of-the-art
methods train two deep networks simultaneously for sample selection, which aims to employ …

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 …

Abstract Deep Neural Networks (DNNs) possess powerful prediction capability thanks to
their over-parameterization design, although the large model complexity makes it suffer from …

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels
usually results in inferior model performance. Existing state-of-the-art methods primarily …

Noisy label learning aims to learn robust networks under the supervision of noisy labels,
which plays a critical role in deep learning. Existing work either conducts sample selection …