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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
Towards robust pattern recognition: A review
The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …
achieving or even outperforming human performance. From the perspective of accuracy …
Selective-supervised contrastive learning with noisy labels
S Li, X ** for learning with noisy labels
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 …
the-art label-noise learning methods. To exploit this property, the early stop** trick, which …
Normalized loss functions for deep learning with noisy labels
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 …
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …
Combating noisy labels by agreement: A joint training method with co-regularization
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" …
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …