每个brach中通过encoder得到隐变量分布,再通过decoder重建数据来近似数据生成过程。 在这篇文章中,作者引入了因果结构模型来刻画 instance-dependent label noise 的生成,通过对数据X和噪声两者生成模式的近似,来促进模型推断真实label的能力。对于弱监督学习具有启发意义。
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...
Instance-dependent label noiseClassificationLogistic regressionLearning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, ...
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...
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,...
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 ...
但是我们发现到大部分的文章在建模label noise时,都采用random noise(symmetric or asymmetric),实际上在真实世界的数据集中,存在更多的是instance-dependent (feature-dependent) label noise,即特征相关的噪音标签。比如标注人员容易把狼标记为狼狗,但是不会轻易把狼标记成桌子。如何让模型对instance-dependent label ...
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...