Learning with noisy labels. In Advances in Neural Information Processing Systems, pages 1196-1204, 2013.Natarajan, N., Dhillon, I., Ravikumar, P., Tewari, A.: Learning with noisy labels. In: Advances in Neural Information Processing Systems 26, pp. 1196-1204 (2013) 3...
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...
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 \4.发表期刊/会议:ICLR \5.发表时间:2020 \6.原...
2013 Learning with noisy labels use meta-learning to train deep models, where synthetic noisy labels were generated to update the model before the conventional gradient update 2019 Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition FL + noisy label: large Ea...
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，...
2013). Because SCAR PU Learning is a specific setting of learning with NAR noisy labels, the SCAR methods can often be generalized to NAR. For example, rebalancing methods, where the instances get class-dependent weights, and empirical-risk-minimization based methods both exists for learning with...
Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy ...
Learning-with-Label-Noise A curated list of resources for Learning with Noisy Labels Papers & Code 2008-NIPS - Whose vote should count more: Optimal integration of labels from labelers of unknown expertise.[Paper][Code] 2009-ICML - Supervised learning from multiple experts: whom to trust when...
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 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....