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 …

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 …

Label noise has been a practical challenge in deep learning due to the strong capability of
deep neural networks in fitting all training data. Prior literature primarily resorts to sample …

Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization
performance. To overcome this problem, we present a simple and effective method self …

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 …

Noisy labels present a significant challenge in deep learning because models are prone to
overfitting. This problem has driven the development of sophisticated techniques to address …

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 real application scenarios, the performance of deep networks may be degraded when the
dataset contains noisy labels. Existing methods for learning with noisy labels are limited by …

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 …

Deep learning algorithms require large amounts of labeled data for effective performance,
but the presence of noisy labels often significantly degrade their performance. Although …