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,...
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...
However, the current IDN approaches fail to consider the typicality of samples, which hampers their ability to address real-world label noise effectively. To alleviate the issues, we introduce typicality- and instance-dependent label noise (TIDN) to simulate real-world noise and establish a TIDN...
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 ...
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...
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...
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 ...
use an observation of measured signal values to generate one or more estimated signal values not present in the observation of measured signal values. When using this method, this field indicates whether the independent variables should represent all of the drivers for the dependent output variables....
However, while for precision we expect to perform in general better than the low-level approach, as explained above, the better performance of the LIG-based approach in terms of simplicity when no additional noise occurs are more dependent on the characteristics of the LIGs. The better ...
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...