Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …

Deep neural networks are known to be annotation-hungry. Numerous efforts have been
devoted to reducing the annotation cost when learning with deep networks. Two prominent …

Training accurate deep neural networks (DNNs) in the presence of noisy labels is an
important and challenging task. Though a number of approaches have been proposed for …

Learning exists in the context of data, yet notions of confidence typically focus on model
predictions, not label quality. Confident learning (CL) is an alternative approach which …

Robust loss functions are essential for training accurate deep neural networks (DNNs) in the
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …

Current deep neural networks (DNNs) can easily overfit to biased training data with
corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to …

Despite being robust to small amounts of label noise, convolutional neural networks trained
with stochastic gradient methods have been shown to easily fit random labels. When there …