Instance-Dependent Label-Noise Learning under Structural Causal Models shjj 玉汝于成/健康工作,快乐生活 来自专栏 · ML Paper Daily 13 人赞同了该文章 Instance-Dependent Label-Noise Learning under Structural Causal Models. NeurIPS'2
Noisy label learningInstance-dependent noiseDeep neural networksClassificationLabel refinement methods are designed to improve the quality of training labels by incorporating model predictions into the original training labels. By adjusting the combination coefficient of the noisy label, the impact of noise ...
如何让模型对instance-dependent label noise 鲁棒不仅在技术上存在着比较多的难题,在理论上也不好建模(和instance-independent相比)。 我们的贡献是提供一个instance-dependent label noise的解决方案并提供最优性的保证。基于自步学习+双网络互相学习(co-teaching)的策略在筛选instance-independent噪音标签上已经比较成熟,...
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evidences showing that we humans perceive instances by decomposing them into parts. Annotators are therefore more likely to...
This code is a PyTorch implementation of our paper "Learning with Instance-Dependent Label Noise: A Sample Sieve Approach" accepted by ICLR2021. The code is run on the Tesla V-100. Prerequisites Python 3.6.9 PyTorch 1.2.0 Torchvision 0.5.0 ...
1. Instance-Dependent Noise (IDN) 1.1. Noisy labels used in this paper In our experiments, we generated noisy labels of IDN for MNIST and CIFAR-10. Here we release the related files. data/CIFAR10/label_noisy/dependent0.1.csv data/CIFAR10/label_noisy/dependent0.2.csv data/CIFAR10/label_noi...
(a). However, it would otherwise be difficult to define or annotate a desired common space and give it a clear semantic interpretation like the low dimensional class label matrixYused in usual cross-modal frameworks. Motivated by several unsupervised cross-modal subspace learning methods [3,20,...
Urtasun. Learning deep structured active contours end-to-end. In CVPR, 2018. [31] E. N. Mortensen and W. A. Barrett. Intelligent scissors for image composition. In SIGGRAPH, pages 191–198, 1995. [32] S. Osher and J. A. Sethian. Fronts propagating with curvat...
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...
原论文标题:Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise 问题引入: 以往对于Label noise的研究大多基于class-conditional noise(CCN)假设,即假设noise标签 y¯ 是与输入的特征 x 无关的,而作者认为这样的假设不符合实际:在Clothing1M真实噪音数据集上进行的计算...