Deep learning with noisy labels is practically challenging, as the capacity of deep models is
so high that they can totally memorize these noisy labels sooner or later during training …

The memorization effects of deep networks show that they will first memorize training data
with clean labels and then those with noisy labels. The early stopping method therefore can …

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 …

Recent deep networks are capable of memorizing the entire data even when the labels are
completely random. To overcome the overfitting on corrupted labels, we propose a novel …

Learning with noisy labels is one of the hottest problems in weakly-supervised learning.
Based on memorization effects of deep neural networks, training on small-loss instances …

Modern neural networks have the capacity to overfit noisy labels frequently found in real-
world datasets. Although great progress has been made, existing techniques are very …

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels,
and it is well-known that modern deep neural networks (DNNs) poorly generalize from such …

Deep neural networks (DNNs) have achieved tremendous success in a variety of
applications across many disciplines. Yet, their superior performance comes with the …

Robust loss functions are essential for training accurate deep neural networks (DNNs) in the
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the
label noise. We observe that the memorization strength of DNNs towards each instance is …