第一种 Clean Data 是比较容易获取的，可以随便找现有的公开数据集，通过模拟置噪的方式来使数据集变成...
This work propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an ...
这次的 paper reading,聚焦 Learning with noisy label: 有一定量的标注数据。-- 通过搜索引擎、公开数据集等,很容易拿到。 标注数据的质量不高,存在或高或低的标注错误。 不会覆盖无监督类学习。 相比于无监督学习,learning with nois...
进行learning with noisy labels 这个领域也有了将近 1 年之久,在此期间读了大量文章,下面来尝试对这些文章做一个总结。 noisy label 的 paper 大合集github.com/guixianjin/noisy_label_papers发布于 2023-05-16 11:01・IP 属地加拿大 深度学习(Deep Learning) 统计学习 机器学习 ...
原文链接:凤⭐尘 》》https://www.cnblogs.com/phoenixash/p/15369008.html 基本信息 \1.标题:DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING \2.作者:Junnan Li, Richard Socher, Steven C.H. Hoi \3.作者单位:Salesforce Research ...
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....
Mohamed-Rafik Bouguelia, Slawomir Nowaczyk, K C Santosh, and Antanas Verikas. 2017. Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int. J. Mach. Learn. Cybern. (2017), pp. 1-13.Bouguelia, M. R., Nowaczyk, S., Santosh, K. C., & Verikas, A. (...
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...
Learning with noisy labels 相关学科: MixMatchCo-CorrectingSharpness-Aware MinimizationEarly StoppingTransition LearningPoint Cloud SegmentationMixupEntropy RegularizationLabel SmoothingLPM 学科讨论 暂无讨论内容,你可以发起讨论推荐文献 发布年度 会议/ 期刊 按被引用数 Learning with Noisy Labels Nagarajan Natarajan...
As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised ...