PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels

7 December 2022

Huaxi Huang

Hui-Sung Kang

Sheng Liu

Olivier Salvado

Thierry Rakotoarivelo

Papers citing "PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels"

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Title
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu 52 33 0 23 Mar 2023
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style Zhuo Huang Xiaobo Xia Li Shen Bo Han Biwei Huang Chen Gong Tongliang Liu OODD 40 47 0 07 Jul 2022
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 36 107 0 28 Feb 2022
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations Jiaheng Wei Zhaowei Zhu Weiran Wang Tongliang Liu Gang Niu Yang Liu NoLa 66 249 0 22 Oct 2021
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations Sheng Liu Kangning Liu Weicheng Zhu Yiqiu Shen C. Fernandez‐Granda NoLa 43 104 0 07 Oct 2021
Instance-dependent Label-noise Learning under a Structural Causal Model Yu Yao Tongliang Liu Biwei Huang Bo Han Gang Niu Kun Zhang CML NoLa 42 70 0 07 Sep 2021
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain Guangyao Chen Peixi Peng Li Ma Jia Li Lin Du Yonghong Tian AAML OOD 35 92 0 19 Aug 2021
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Zeren Sun Yazhou Yao Xiu-Shen Wei Yongshun Zhang Fumin Shen Jianxin Wu Jian Zhang Heng Tao Shen 36 56 0 05 Aug 2021
Deep Learning on a Data Diet: Finding Important Examples Early in Training Mansheej Paul Surya Ganguli Gintare Karolina Dziugaite 79 446 0 15 Jul 2021
Understanding and Improving Early Stopping for Learning with Noisy Labels Ying-Long Bai Erkun Yang Bo Han Yanhua Yang Jiatong Li Yinian Mao Gang Niu Tongliang Liu NoLa 31 216 0 30 Jun 2021
EfficientNetV2: Smaller Models and Faster Training Mingxing Tan Quoc V. Le EgoV 66 2,662 0 01 Apr 2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin B. Guo ViT 218 21,051 0 25 Mar 2021
Augmentation Strategies for Learning with Noisy Labels Kento Nishi Yi Ding Alex Rich Tobias Höllerer NoLa 29 117 0 03 Mar 2021
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain Honggu Liu Xiaodan Li Wenbo Zhou YueFeng Chen Yuan He Hui Xue Weiming Zhang Nenghai Yu CVBM 195 369 0 02 Mar 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 61 148 0 11 Feb 2021
A Survey on Visual Transformer Kai Han Yunhe Wang Hanting Chen Xinghao Chen Jianyuan Guo ... Chunjing Xu Yixing Xu Zhaohui Yang Yiman Zhang Dacheng Tao ViT 77 2,174 0 23 Dec 2020
A Second-Order Approach to Learning with Instance-Dependent Label Noise Zhaowei Zhu Tongliang Liu Yang Liu NoLa 52 127 0 22 Dec 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 182 40,217 0 22 Oct 2020
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Hao Cheng Zhaowei Zhu Xingyu Li Yifei Gong Xing Sun Yang Liu NoLa 45 205 0 05 Oct 2020
Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu Jonathan Niles-Weed N. Razavian C. Fernandez‐Granda NoLa 51 561 0 30 Jun 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 334 508 0 05 Mar 2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Junnan Li R. Socher Guosheng Lin NoLa 59 1,021 0 18 Feb 2020
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates Yang Liu Hongyi Guo NoLa 33 238 0 08 Oct 2019
SELF: Learning to Filter Noisy Labels with Self-Ensembling Philipp Kratzer Marc Toussaint Thi Phuong Nhung Ngo T. Nguyen Jim Mainprice Thomas Brox NoLa 59 314 0 04 Oct 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 53 375 0 01 Jun 2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 43 518 0 28 May 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 48 17,950 0 28 May 2019
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 85 172 0 24 May 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel 114 3,009 0 06 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 72 1,825 0 06 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 55 609 0 25 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 89 352 0 27 Mar 2019
How does Disagreement Help Generalization against Label Corruption? Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W. Tsang Masashi Sugiyama NoLa 29 778 0 14 Jan 2019
Robustness of Conditional GANs to Noisy Labels Kerry J. Halupka A. Khetan Zinan Lin Stephen Moore NoLa 49 81 0 08 Nov 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 50 2,580 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 73 2,051 0 18 Apr 2018
Iterative Learning with Open-set Noisy Labels Yisen Wang Weiyang Liu Xingjun Ma James Bailey H. Zha Le Song Shutao Xia NoLa 49 326 0 31 Mar 2018
Joint Optimization Framework for Learning with Noisy Labels Daiki Tanaka Daiki Ikami T. Yamasaki Kiyoharu Aizawa NoLa 55 708 0 30 Mar 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 107 1,419 0 24 Mar 2018
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 83 1,448 0 14 Dec 2017
Learning with Biased Complementary Labels Xiyu Yu Tongliang Liu Biwei Huang Dacheng Tao 46 195 0 27 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 219 9,687 0 25 Oct 2017
WebVision Database: Visual Learning and Understanding from Web Data Wen Li Limin Wang Wei Li E. Agustsson Luc Van Gool VLM 57 435 0 09 Aug 2017
A Closer Look at Memorization in Deep Networks Devansh Arpit Stanislaw Jastrzebski Nicolas Ballas David M. Krueger Emmanuel Bengio ... Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio Simon Lacoste-Julien TDI 88 1,801 0 16 Jun 2017
Decoupling "when to update" from "how to update" Eran Malach Shai Shalev-Shwartz NoLa 53 564 0 08 Jun 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 75 1,444 0 13 Sep 2016
Deep Networks with Stochastic Depth Gao Huang Yu Sun Zhuang Liu Daniel Sedra Kilian Q. Weinberger 121 2,344 0 30 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.1K 192,638 0 10 Dec 2015
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen A. Menon Robert C. Williamson NoLa 60 309 0 28 May 2015
Training Deep Neural Networks on Noisy Labels with Bootstrapping Scott E. Reed Honglak Lee Dragomir Anguelov Christian Szegedy D. Erhan Andrew Rabinovich NoLa 91 1,014 0 20 Dec 2014