the variational difference term defined with noisy labels is an affine transformation of the clean variational difference, subject to an addition of a bias term
Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN)...
In real-world datasets, noisy labels are pervasive. The challenge of learning with noisy labels (LNL) is to train a classifier that discerns the actual classes from given instances. For this, the model must identify features indicative of the authentic labels. While research indicates that ...
尽管LNL和SSL各自取得了进步,但它们之间的联系尚未得到充分的探索。在这项工作中,我们提出了DivideMix,它以半监督的方式处理标签噪声的学习。与大多数现有的LNL方法不同,DivideMix丢弃了极有可能是噪声的样本标签,利用噪声样本作为未标记数据,使模型从过拟合中得到正则化,提高泛化性能。这项工作的主要贡献是: 我们提出c...
相比于无监督学习,learning with noisy label 更贴近深度学习在工业界的落地。典型的状态如下: 初始阶段有一定量的标注质量未知的数据。 一般会有持续的人工投入,不断提升标注质量。人工投入的形式,可能是付费众包,可能是借助用户反馈。 对...
为了解决这个问题,文章提出了 twin contrastive learning,它将 lable-free 的无监督表征学习和 label-noisy 的标记相结合,利用对比学习来学习判别性的表征,并基于表征构建一个GMM,需要注意的是,这个GMM不同于常规的无监督GMM,它用模型预测代替了GMM的隐变量,从而将无监督表征的学习和标记联系起来了。然后,根据学习到...
Learning with Noisy Labels (LNL) The main running file ismain.pywith arguments as follows: noise_type: symmetric | asymmetric noise_rate: noise rate loss: AGCE | AUL | AEL | CE (Cross Entropy) | FL (Focal Loss) | MAE | GCE | SCE | NFL | NCE | ... ...
In this paper, we propose a new task, in-context learning with noisy labels, which aims to solve real-world problems for in-context learning where labels in task demonstrations would be corrupted. Moreover, we propose a new method and baseline methods for the new task, inspired by studies ...
Learning with Noisy Labels via Sparse Regularization Xiong Zhou1,2 Xianming Liu1,2* Chenyang Wang1 Deming Zhai1 Junjun Jiang1,2 Xiangyang Ji3 1Harbin Institute of Technology 2Peng Cheng Laboratory 3Tsinghua University {cszx,csxm,cswcy,zhaideming,junjunjiang}@hit.edu.cn xyji@tsinghua...
2.1 LEARNING WITH NOISY LABELS 现有的训练带噪声标签的dnn的方法大都是为了修正loss函数。修正方法可以分为两类。第一种方法对所有样本一视同仁,通过重新标记噪声样本来显式或隐式地纠正损失。对于重标记方法,对噪声样本的建模采用有向图模型(Xiao et al., 2015)、条件随机场(Vahdat, 2017)、知识图(Li et al...