Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …

We propose a novel framework to perform classification via deep learning in the presence of
noisy annotations. When trained on noisy labels, deep neural networks have been observed …

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" …

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 …

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 …

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 …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Learning from noisy data is a challenging task that significantly degenerates the model
performance. In this paper, we present TCL, a novel twin contrastive learning model to learn …

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 …

In label-noise learning, estimating the transition matrix has attracted more and more
attention as the matrix plays an important role in building statistically consistent classifiers …