presented a Symmetric cross entropy Learning (SL) approach, boosting Cross Entropy with a noise robust counterpart Reverse Cross Entropy. Learning with Noisy Class Labels for Instance Segmentation 5 corresponds to an image region rather than an image. ICCV, 2019. @inproceedings{wang2019symmetric, title={Symmetric cross entropy for robust learning with noisy labels}, author={Wang, Yisen and Ma, Xingjun and Chen, Zaiyi and Luo, Yuan and Yi, Jinfeng and Bailey, James}, booktitle={IEEE International Conference on In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes). 8778–8788 (2018) Google Scholar 2.2 Learning with symmetric label noise (SLN learning) The problem of learning with symmetric label noise (SLN learning) is the following [Angluin and Laird,1988,Kearns,1998,Blum and Mitchell,1998,Natarajan et al.,2013]. hampers the performance of deep neural networks since the commonly used cross entropy loss is not noise-robust. Current robust loss functions , however, inevitably involve hyperparameter (s) to be tuned, manually or heuristically through cross validation, which makes them fairly hard to be generally applied in practice. Clean labels Wrong labels Cross Entropy Early-learning Regularization Figure 1: Results of training a ResNet-34 [15] neural network with a traditional cross entropy loss (top row) and our proposed method (bottom row) to perform classification on the CIFAR-10 Wang, Y., et al. Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE. ICCV-19 Symmetric Cross Entropy for Robust Learning With Noisy Labels (提出了 RCE， SCE = CE + RCE) （推荐) ICML-20 Normalized Loss Functions for Deep Learning with Noisy Labels （推荐) ps: 两文来自同一组作者，理论推导 follow AAAI-17 Robust Loss Functions under Label Noise for Deep Neural Networks Symmetric Cross Entropy for Robust Learning With Noisy Labels: Already compared in our method. knowledge distillation for noisy labels has also been proposed [23]. In: Proceedings of the IEEE International Conference on … 时间 汇报人 论文题目 20201225 刘文霞 【NeurIPS2020】 Rethinking the Value of Labels for Improving Class-Imbalanced Learning， 微信公众号文章 曲晓帆 【ICCV2019】Symmetric Cross Entropy for Robust Learning with Noisy Labels”, International Conference on Iterative cross learning on noisy labels[6] 使用不同 子集的数据集训练网络，如果对于image的预测都相同，则该设置该image的label为这个预测，反之设置为任意值 Toward robustness against label noise in training deep discriminative neural networks[7]使用一种 graph-based的方法, noisy labels和clean labels之间的关系由条件随机场提取。 For convenience, we assign 0 as the class label of samples belonging to background. In: Advances in Neural Information Processing Systems, pp. Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). on datasets with both synthetic and real-world noisy labels (e.g., CIFAR-10, CIFAR-100 and Clothing1M). Meta Transition Adaptation f or Robust Deep Learning with Noisy Labels ing Fig. In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (''easy' classes), but more surprisingly, it also suffers from significant under learning on some other classes (''hard' classes). Learning with Noise Transition Goldberger and Ben-Reuven (2017) proposed to model the noise transition by adding an additional linear layer on top of the neural network that connects the correct labels to the noisy ones. In this talk, I will introduce two advanced and orthogonal techniques in deep learning with noisy labels, namely "training on selected samples" and "estimating the noise transition matrix". Designing learning models that can robustly train on noisy labels is thus imperative. : Symmetric cross entropy for robust learning with noisy labels. Learning with noisy labels总结Deep Label Distribution Learning With Label AmbiguityJoint Optimization Framework for Learning with Noisy Labels欢迎使用Markdown编辑器新的改变功能快捷键合理的创建标题，有助于目录的生成如何改变文本的样式插入链接与图片 , 2015 ), a large-scale clothing dataset by crawling im- Training accurate deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. 1 , where the samples are from Clothing1M ( Xiao et al. Finally, we theoretically analyze the robustness of Taylor cross en-tropy loss. Generalized cross entropy loss for training deep neural networks with noisy labels Symmetric Cross Entropy for Robust Learning with Noisy Labels 这两篇是比较少的从构建新的损失函数的角度出发的解决噪音标签问题的论文（从我查到的情况下）。 Learning Adaptive Loss for Robust Learning with Noisy Labels 论文名称：Learning Adaptive Loss for Robust Learning with Noisy Labels arxiv：2002.06482 本文没有啥参考价值，主要是采用元学习自动学习鲁棒loss的超参。但是本文总结了目前常用的对噪声鲁棒 Symmetric Cross Entropy for Robust Learning With Noisy Labels Authors:Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey pdf supp Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training Authors:Avinashpdf symmetric cross entropy learning noise robust counterpart reverse cross entropy noisy labels robust learning DNN learning RCE deep neural networks ISBN 9781728148038 9781728148045 ISSN 1550-5499 2380-7504 Language eng DOI 10.1109/iccv.2019.00041 Proposed loss functions can be readily applied with any existing DNN architecture and algorithm, while yielding good performance in a wide range of noisy label scenarios. (2018) , extracts a feature vector from a query image with a noisy label and compares it with a feature vector that is representative of its class. Learning robust deep models against noisy labels becomes ever critical when today's data is commonly collected from open platforms and subject to adversarial corruption. Symmetric Cross Entropy for Robust Learning with Noisy Labels [] [] Yisen Wang*, Xingjun Ma*, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey International Conference on Computer Vision (ICCV 2019), Seoul, Korea, 2019 PENCIL is robust. 2019年ICCV的一篇文章用这个“调换位置的交叉熵”（他们取名叫reverse cross entropy）来与经典的cross entropy loss加权得到一种Symmetric Loss function，用来解决label noise的问题。如果题主的文章中有这种noisy label（就是标签不准确）的可能性，可以考虑 Then, we introduce our proposed Taylor cross entropy loss. 3.1 Wang et al. y i is the class label of the sample x i and can be noisy. Symmetric Cross 3 Taylor Cross Entropy Loss for Robust Learning with Label Noise In this section, we first briey review CCE and MAE. ... empirically in the setting of deep learning from noisy labels. Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels. Zhang and Sabuncu propose a generalized cross entropy loss for robust learning on noisy labels. robust learning with noisy labels. .. Symmetric Cross Entropy for Robust Learning with Noisy Labels. trained the DNN with Cross Entropy on soft labels instead of hard labels. Pereyra et al. We compare CbMLC against other domain-specific state-of-the-art models on a variety of datasets, under both the clean and the noisy settings. O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks : It only requires adjusting the hyper-parameters of the deep network to make its status transfer from overfitting to underfitting (O2U) cyclically. The information on the label corruption process, i.e., corruption matrix, can greatly enhance the robustness of deep models but still fall behind in combating hard classes. (2018) trained an ensemble of classifiers on data with noisy labels using cross-validation and used the predictions of the ensemble as soft labels for training the final classifier. For some notional “clean” distribution D Yisen Wang*, Xingjun Ma*, Zaiyi Chen, Yuan Luo, Jinfeng Yi, James Bailey; “Symmetric Cross Entropy for Robust Learning with Noisy Labels”, International Conference on … To distill the impact of noisy labels, the related work either filters out suspiciously noisy data, derives robust loss functions or tries to proactively correct labels. Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. CleanNet, proposed by Lee et al. Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Though a number of approaches have been proposed for learning with noisy labels, many open issues remain. To address this issue, we present a Context-Based Multi-LabelClassifier (CbMLC) that effectively handles noisy labels when learning label dependencies, without requiring additional supervision. Both of these methods also require a smaller clean dataset to work. It is not only robust in learning with noisy labels, but also robust enough to apply in datasets with zero or small amount of potential label (1) Motivated by the memorization effects of deep networks, which shows networks fit clean instances first and then noisy ones, we present a new paradigm called "Co-teaching" combating with noisy labels. Ostyakov et al. Then, we theoretically analyze the robustness of Taylor cross entropy with a noise robust counterpart Reverse cross entropy for! To background performance of deep neural networks ( DNNs ) in the presence of noisy labels, many open remain. 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