Designing robust loss functions is popular in learning with noisy labels while existing
designs did not explicitly consider the overfitting property of deep neural networks (DNNs) …

Recent studies on the memorization effects of deep neural networks on noisy labels show
that the networks first fit the correctly-labeled training samples before memorizing the …

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

Since convolutional neural networks (ConvNets) can easily memorize noisy labels, which
are ubiquitous in visual classification tasks, it has been a great challenge to train ConvNets …

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly
training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize …

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 …

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 …

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 …

Training deep neural networks (DNNs) with noisy labels often leads to poorly generalized
models as DNNs tend to memorize the noisy labels in training. Various strategies have been …

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