Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-
the-art label-noise learning methods. To exploit this property, the early stop** trick, which …

Deep Learning has achieved remarkable successes in many industry applications and
scientific research fields. One essential reason is that deep models can learn rich …

Learning with the\textit {instance-dependent} label noise is challenging, because it is hard to
model such real-world noise. Note that there are psychological and physiological evidences …

Abstract Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that
dominate the field of medical image-to-image translation. However, neither modes are ideal …

Partial label learning (PLL) is an important problem that allows each training example to be
labeled with a coarse candidate set, which well suits many real-world data annotation …

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 …

In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and
clean data, has been widely exploited to learn statistically consistent classifiers. The …

The label transition matrix has emerged as a widely accepted method for mitigating label
noise in machine learning. In recent years, numerous studies have centered on leveraging …