1.4 《Learning with Bounded Instance- and label-dependent Label Noise》 This paper focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of Label-dependent label Noise where the label noise rates. This paper focus on a particular case of ILN where noise rates h...
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Noisy label training is the problem of training a neural network from a dataset with errors in the labels. Selective prediction is the problem of selecting only the predictions of a neural network which have sufficient confidence. These problems are both important in medical deep learning, where ...
Deep Learning with Noisy Label 背景理想状态下,深度学习依赖大量高质量标注,时间&人力成本高往往数据标注质量往往并不处于理想状态,噪声不可避免算法分类基于噪声模型的方法:把分类器和噪声隔离开,希望通过噪声… 资瓷向量机发表于搬砖杂记 [CVPR2023] Twin Contrastive Learning with Noisy Labels Breann Introductio...
- new applications where label noise must be taken into account; - theoretical results about learning in the presence of label noise; - experimental results which provide insight about existing methods; - dealing with different types of label noise (random, non-random, malicious, or adversarial);...
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Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. Thi...
Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is ind...
In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying ...