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 …

Abstract Deep Neural Networks (DNNs) have demonstrated remarkable performance across
diverse domains and tasks with large-scale datasets. To reduce labeling costs for large …

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" …

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 …

Deep learning algorithms require large amounts of labeled data for effective performance,
but the presence of noisy labels often significantly degrade their performance. Although …

Learning with noisy labels has aroused much research interest since data annotations,
especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to …

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 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 …

Recent advances in deep learning have relied on large, labelled datasets to train high-
capacity models. However, collecting large datasets in a time-and cost-efficient manner …

Learning with noisy-labels has become an important research topic in computer vision
where state-of-the-art (SOTA) methods explore: 1) prediction disagreement with co-teaching …