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 label。这个过程是为了使noise label与x相关。...
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...
CE (Cross Entropy)和CORES在symmetric label noise和instance-based label noise下的表现, 可以看到CE很容易拟合instance-based label noise,但是CORES的分离能力依旧良好。 CORES和co-teaching方法在instance-based label noise下的表现,可以看到co-teaching 在noise rate比较高时,F-score(筛选样本纯度的度量)很低,然后...
However, previous label refinement methods are unable to model instance-dependent noise, which is the most realistic type of noise. To address this limitation, we propose a simple approach, probabilistic instance-dependent label refinement (referred to as 蟺 -LR). Inspired by the fact that humans...
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...
Run CORES (Phase 2: Consistency Training) on the CIFAR-10 with instance 0.6 noise: cd phase2 CUDA_VISIBLE_DEVICES=0,1 python phase2.py -c confs/resnet34_ins_0.6.yaml --unsupervised Both Phase 1 and Phase 2 do not need pre-trained model. ...
aa place or condition of wild noise and confusion 狂放的噪声和混乱的地方或情况 [translate] a2. 打(领带)。 [translate] a当妈妈做晚饭是我经常帮忙 When mother makes the dinner is I helps frequently [translate] aAtomic force microscope (AFM) cantilevers are known to be very sensitive to ...
Noise Ratio Type Best Accuracy [Early Stopping Epoch] Mini-batch Saved Noise Ratio Type Baseline IES Mini-batch Saved 20% Symmetric 55.39% [17] 55.39% [17] 0% 40% Symmetric 43.87% [15] 43.87% [15] 0% 20% Instance 57.30% [18] 57.30% [18] 0% 40% Instance 47.67% [18] 47.67% [...
Current state-of-the-art methods for dealing with instance-dependent noise focus on data-recalibrating strategies to iteratively correct labels while training the network. While some methods provide theoretical analysis to prove that each iteration results in a cleaner dataset and a better-performing ...