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Meta Transition Adaptation for Robust Deep Learning with Noisy Labels

10 June 2020

Papers citing "Meta Transition Adaptation for Robust Deep Learning with Noisy Labels"

36 / 36 papers shown

Title
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 520 42,559 0 03 Dec 2019
Human uncertainty makes classification more robust Joshua C. Peterson Ruairidh M. Battleday Thomas Griffiths Olga Russakovsky OOD 64 304 0 19 Aug 2019
Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia Tongliang Liu N. Wang Bo Han Chen Gong Gang Niu Masashi Sugiyama NoLa 73 379 0 01 Jun 2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels Pengfei Chen B. Liao Guangyong Chen Shengyu Zhang NoLa 67 389 0 13 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 84 614 0 25 Apr 2019
How does Disagreement Help Generalization against Label Corruption? Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W. Tsang Masashi Sugiyama NoLa 65 783 0 14 Jan 2019
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 92 261 0 29 Oct 2018
Small Sample Learning in Big Data Era Jun Shu Zongben Xu Deyu Meng 83 72 0 14 Aug 2018
Bilevel Programming for Hyperparameter Optimization and Meta-Learning Luca Franceschi P. Frasconi Saverio Salzo Riccardo Grazzi Massimiliano Pontil 179 732 0 13 Jun 2018
Do CIFAR-10 Classifiers Generalize to CIFAR-10? Benjamin Recht Rebecca Roelofs Ludwig Schmidt Vaishaal Shankar OOD FedML ELM 168 414 0 01 Jun 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 85 2,608 0 20 May 2018
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Bo Han Quanming Yao Xingrui Yu Gang Niu Miao Xu Weihua Hu Ivor Tsang Masashi Sugiyama NoLa 115 2,078 0 18 Apr 2018
Joint Optimization Framework for Learning with Noisy Labels Daiki Tanaka Daiki Ikami T. Yamasaki Kiyoharu Aizawa NoLa 74 712 0 30 Mar 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 146 1,430 0 24 Mar 2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise Dan Hendrycks Mantas Mazeika Duncan Wilson Kevin Gimpel NoLa 134 556 0 14 Feb 2018
Stronger generalization bounds for deep nets via a compression approach Sanjeev Arora Rong Ge Behnam Neyshabur Yi Zhang MLT AI4CE 86 643 0 14 Feb 2018
Size-Independent Sample Complexity of Neural Networks Noah Golowich Alexander Rakhlin Ohad Shamir 154 548 0 18 Dec 2017
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels Lu Jiang Zhengyuan Zhou Thomas Leung Li Li Li Fei-Fei NoLa 106 1,456 0 14 Dec 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 282 9,797 0 25 Oct 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 83 609 0 29 Jul 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 207 1,224 0 26 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,117 0 19 Jun 2017
Decoupling "when to update" from "how to update" Eran Malach Shai Shalev-Shwartz NoLa 85 567 0 08 Jun 2017
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks Arash Vahdat NoLa 92 298 0 31 May 2017
Learning Deep Networks from Noisy Labels with Dropout Regularization Ishan Jindal M. Nokleby Xuewen Chen NoLa 61 184 0 09 May 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang Erik Learned-Miller Andrew McCallum 75 354 0 24 Apr 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 825 11,937 0 09 Mar 2017
Learning from Noisy Labels with Distillation Yuncheng Li Jianchao Yang Yale Song Liangliang Cao Jiebo Luo Li Li NoLa 80 550 0 07 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 345 4,629 0 10 Nov 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 110 1,458 0 13 Sep 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 545 5,909 0 08 Jul 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 886 27,412 0 02 Dec 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 362 19,723 0 09 Mar 2015
Label Distribution Learning Xin Geng 59 527 0 26 Aug 2014
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising Gilles Blanchard Marek Flaska G. Handy Sara Pozzi Clayton Scott NoLa 103 244 0 05 Mar 2013