CleanData,q表示真值的真实分布,p表示分类器预测结果的分布 NoisyData,q不是真值的真实分布,p一定程度上可以表示真值的真实分布 反向KL散度,用p做监督: ICML2020: Normalized Loss Functions for Deep Learning with Noisy Labels Normalized Loss Functions,并证明noise-torelant Robustness alone is not sufficient Re...
Deep learning with noisy labels in medical prediction problems: ascoping reviewYishu Wei, Ph.D. 1,3 , Yu Deng, Ph.D. 2 , Cong Sun, Ph.D. 1 , Mingquan Lin, Ph.D. 1 , Hongmei Jiang, Ph.D. 4 ,and Yifan Peng, Ph.D. 1,*1 Department of Population Health Sciences, Weill Cornel...
deep learningnoisy labellabel uncertaintyOBJECTIVES. Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To...
Deep learning with noisy labels: exploring techniques and remedies in medical image analysisLabel noiseDeep learningMachine learningBig dataMedical image annotationSupervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical ...
上周读了几篇关于如何处理noisy label的论文,这里记录一下对于论文Deep Self-Learning From Noisy Labels的一些理解以及自己的代码实现。 文中主要提出了一个矫正noisy label的方法,以及如果利用这些矫正过的标签。从上图可以看出,整个流程分为两个部分,上半部分其实就是普通的分类网络,网络结构任意,只是在计算loss时...
We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning. PDF Abstract ...
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significant...
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 high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of...
Code for ICML2020 Paper"Normalized Loss Functions for Deep Learning with Noisy Labels" Requirements Python >= 3.6, PyTorch >= 1.3.1, torchvision >= 0.4.1, mlconfig How To Run Configs for the experiment settings Check '*.yaml' file in the config folder for each experiment. ...
原文:[2106.09291v1] Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion (arxiv.org) Abstract 在过去的几年中,一些处理有噪声标签的深度学习方法已经被发展出来,其中许多都是基于小损失标准的。然而,很少有理论分析来解释为什么这些方法可以很好地从噪声标签中学习。