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 …

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 has achieved excellent performance in various computer vision tasks, but
requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy …

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 …

The efficacy of deep learning depends on large-scale data sets that have been carefully
curated with reliable data acquisition and annotation processes. However, acquiring such …

Sample selection is a prevalent method in learning with noisy labels, where small-loss data
are typically considered as correctly labeled data. However, this method may not effectively …

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 Learning with noisy labels is a practically challenging problem in weakly-supervised
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …

Unreliable labels derived from large-scale dataset prevent neural networks from fully
exploring the data. Existing methods of learning with noisy labels primarily take noise …

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 …