Instance-dependent cost-sensitive learning addresses classification problems where each observation has a different misclassification cost. In this paper, we propose cost-sensitive parametric models to minimize
As an inevitable issue in annotating large-scale datasets, instance-dependent label noise (IDN) can cause serious overfitting in neural networks. To combat IDN, label reconstruction methods have been developed with noise transition matrices or DNNs to simulate the transition from clean labels to ...
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 label。这个过程是为了使noise la...
To further improve the efficiency, we propose an Instance-dependent Early Stopping (IES) method that adapts the early stopping mechanism from the entire training set to the instance level, based on the core principle that once the model has mastered an instance, the training on it should stop....
一般来说,最新的大部分 instance dependent 好像大多都是 gap dependent 2021-08-10 回复1 紫杉 ZehaoDou 窦泽皓 啊,囧,所以instance-dependent是指我们进一步在MDP或者Bandit下引入一些assumption然后来收紧bound。。然后gap-dependent是直接假设agent的action prob分布和optimal action只相差一个gap区间。。大概是...
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...
To further improve the efficiency, we propose an Instance-dependent Early Stopping (IES) method that adapts the early stopping mechanism from the entire training set to the instance level, based on the core principle that once the model has mastered an instance, the training on it should stop....
Moreover, for a precise approximation of the instance-dependent noise transition matrix, we calculate the inter-class correlation matrix using only mini-batch samples rather than the entire training dataset. Third, we transform the noisy posterior probability into instance-dependent LD by multiplying ...
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 ...