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...
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...
In this paper, we focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of ILN where the label noise rates - the probabilities that the true labels of examples flip into the corrupted ones - have upper bound less than 1. Specifically, we introduce the ...
Applying existing results from instance-independent settings would require a significant amount of estimation of noise rates. Therefore, learning with instance-dependent label noise remains a challenge. In this paper, we propose CORES^2 (COnfidence REgularized Sample Sieve), which progressively sieves ...
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021) - haochenglouis/cores
第一种 Clean Data 是比较容易获取的，可以随便找现有的公开数据集，通过模拟置噪的方式来使数据集变成...
2020-ICML - Learning with Bounded Instance-and Label-dependent Label Noise.[Paper] 2020-ICML - Label-Noise Robust Domain Adaptation.[Paper] 2020-ICML - LTF: A Label Transformation Framework for Correcting Label Shift.[Papeer] 2020-ICML - Does label smoothing mitigate label noise?.[Paper] ...
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...
上述将两类进行置换的情况叫做pairwise label noise,再引申一下: Symmetry Label Noise:通过将给定比例的训练样本的标签统一翻转到其他类标签之一来生成对称噪声标签。 综合起来,我们举一个Co-teaching论文里面的例子: 2) Instance-dependent Label Noise 样本相关的噪声,即样本标签的噪声和样本本身的特征有关, “你妈...
以往针对标记噪声学习的工作,往往基于一些较强的假设,如标记噪声是label-dependent的而不是instance-dependent的:P(Y~|X,Y)=P(Y~|Y)。这让噪声分布的建模变得容易,但是这种简化难以刻画真实世界的标记噪声。 这篇文章引入了因果结构模型, 因果结构模型