Deep neural networks (DNNs) have achieved excellent performance in image classification
research, part of which is due to the large-scale training data with accurate annotations …

It is well known that deep learning depends on a large amount of clean data. Because of
high annotation cost, various methods have been devoted to annotating the data …

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" …

Despite excellent performance in image classification researches, the training of the deep
neural networks (DNN) needs a large set of clean data with accurate annotations. The …

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for
training deep neural networks (DNNs) robust to label noise have used two primary …

Learning with noisy labels is a vital topic for practical deep learning as models should be
robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning …

In recent years, the development of deep learning has contributed to various areas of
machine learning. However, deep learning requires a huge amount of data to train the …

In recent years, deep neural networks (DNNs) have gained remarkable achievement in
computer vision tasks, and the success of DNNs often depends greatly on the richness of …

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 …

Noisy label learning aims to train deep neural networks using a large amount of samples
with noisy labels, whose main challenge comes from how to deal with the inaccurate …