首先作者使用双视图的backbone进行图像分类,得到每个视图的置信度(confidence),然后提出动态阈值策略,基于每个实例在先前epoch中的记忆强度来选择和修正noise label。受益于动态阈值策略与双视图训练,我们可以根据每个epoch双视图预测的一致性以及与given label的差异,将数据集划分为干净样本集(Clean),困难样本集(Hard),噪声...
instance-dependent noise原理 Instance-Dependent Noise (IDN)是一种处理噪声标签的策略,其原理基于假设在真实标签y给定时,noise标签y¯和输入的特征x是相关的。具体来说,IDN利用DNN(深度神经网络)在没有label noise的数据集上训练的过程,将DNN中较难训练的实例和类联系起来,从而计算误标记的得分和潜在的noisy ...
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...
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, ...
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...
Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent from features. Practically, annotations errors tend to be instance-dependent and often depend on the difficulty levels of recognizing a certain task. ...
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 ...
When using this method, this field indicates whether the independent variables should represent all of the drivers for the dependent output variables. Model Tag ID Automatically generated unique ID for the tag. This value cannot be modified. Notes in Instance Notes about the analytic instance. ...
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 ...
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...