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)...
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 means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data....
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 ...
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...
第二种 Real-World Noisy Datasets 是需要特定的数据集的，它对于数据集制作者来说可能是成本不高的，...
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 | ... ...
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...
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...
Learning from noisy labels [1], [2], [3], [4], [5], [6] for deep models is a challenging problem in practice. Since data noise is ubiquitous in the real world, collecting training data with clean labels would be resource-intensive, especially for some domains with ambiguous labels, ...