Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks

27 March 2019

Papers citing "Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks"

50 / 176 papers shown

Title
Contrastive Representations for Label Noise Require Fine-Tuning Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols 15 1 0 20 Aug 2021
Confidence Adaptive Regularization for Deep Learning with Noisy Labels Yangdi Lu Yang Bo Wenbo He NoLa 24 10 0 18 Aug 2021
Implicit Sparse Regularization: The Impact of Depth and Early Stopping Jiangyuan Li Thanh V. Nguyen C. Hegde R. K. Wong 14 29 0 12 Aug 2021
Learning with Noisy Labels via Sparse Regularization Xiong Zhou Xianming Liu Chenyang Wang Deming Zhai Junjun Jiang Xiangyang Ji NoLa 26 51 0 31 Jul 2021
Adaptive Precision Training (AdaPT): A dynamic fixed point quantized training approach for DNNs Lorenz Kummer Kevin Sidak Tabea Reichmann Wilfried Gansterer MQ 24 5 0 28 Jul 2021
A Tale Of Two Long Tails Daniel D'souza Zach Nussbaum Chirag Agarwal Sara Hooker 29 22 0 27 Jul 2021
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel Dominic Richards Ilja Kuzborskij 13 28 0 27 Jul 2021
An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu Xander Masotto Vandana Bachani Balaji Lakshminarayanan Jack Nikodem Dong Yin NoLa 11 24 0 23 Jul 2021
Disparity Between Batches as a Signal for Early Stopping Mahsa Forouzesh Patrick Thiran 39 7 0 14 Jul 2021
Generalization by design: Shortcuts to Generalization in Deep Learning P. Táborský Lars Kai Hansen OOD AI4CE 10 0 0 05 Jul 2021
A Theoretical Analysis of Fine-tuning with Linear Teachers Gal Shachaf Alon Brutzkus Amir Globerson 34 17 0 04 Jul 2021
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning Pan Zhou Caiming Xiong Xiaotong Yuan S. Hoi SSL 19 12 0 28 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 27 44 0 17 Jun 2021
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels Seulki Park Hwanjun Song Daeho Um D. Jo Sangdoo Yun J. Choi NoLa 26 0 0 14 Jun 2021
Towards the Memorization Effect of Neural Networks in Adversarial Training Han Xu Xiaorui Liu Wentao Wang Wenbiao Ding Zhongqin Wu Zitao Liu Anil K. Jain Jiliang Tang TDI AAML 24 6 0 09 Jun 2021
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path X. Y. Han V. Papyan D. Donoho AAML 28 136 0 03 Jun 2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Biwei Huang Jun Yu Gang Niu Masashi Sugiyama NoLa 17 110 0 01 Jun 2021
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation M. Belkin 14 182 0 29 May 2021
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network Shuo Yang Erkun Yang Bo Han Yang Liu Min Xu Gang Niu Tongliang Liu NoLa BDL 29 42 0 27 May 2021
Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks Guy Hacohen D. Weinshall 16 10 0 12 May 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization Saurabh Garg Sivaraman Balakrishnan J. Zico Kolter Zachary Chase Lipton 28 30 0 01 May 2021
Generalization Guarantees for Neural Architecture Search with Train-Validation Split Samet Oymak Mingchen Li Mahdi Soltanolkotabi AI4CE OOD 36 13 0 29 Apr 2021
Memorisation versus Generalisation in Pre-trained Language Models Michael Tänzer Sebastian Ruder Marek Rei 94 50 0 16 Apr 2021
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks Karim Huesmann Luis Garcia Rodriguez Lars Linsen Benjamin Risse 19 3 0 13 Apr 2021
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels Evgenii Zheltonozhskii Chaim Baskin A. Mendelson A. Bronstein Or Litany SSL 33 92 0 25 Mar 2021
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces Tao Li Lei Tan Qinghua Tao Yipeng Liu Xiaolin Huang 37 10 0 20 Mar 2021
Asymptotics of Ridge Regression in Convolutional Models Mojtaba Sahraee-Ardakan Tung Mai Anup B. Rao Ryan Rossi S. Rangan A. Fletcher MLT 16 2 0 08 Mar 2021
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training Sheng Liu Xiao Li Yuexiang Zhai Chong You Zhihui Zhu C. Fernandez‐Granda Qing Qu 17 25 0 01 Mar 2021
Multiplicative Reweighting for Robust Neural Network Optimization Noga Bar Tomer Koren Raja Giryes OOD NoLa 13 9 0 24 Feb 2021
FINE Samples for Learning with Noisy Labels Taehyeon Kim Jongwoo Ko Sangwook Cho J. Choi Se-Young Yun NoLa 30 103 0 23 Feb 2021
Provable Super-Convergence with a Large Cyclical Learning Rate Samet Oymak 33 12 0 22 Feb 2021
Learning to Combat Noisy Labels via Classification Margins Jason Lin Jelena Bradic NoLa 34 7 0 01 Feb 2021
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning Lang Huang Chaoning Zhang Hongyang R. Zhang SSL 33 24 0 21 Jan 2021
Phases of learning dynamics in artificial neural networks: with or without mislabeled data Yu Feng Y. Tu 25 2 0 16 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise Spencer Frei Yuan Cao Quanquan Gu FedML MLT 64 19 0 04 Jan 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 32 2 0 04 Jan 2021
Identifying Training Stop Point with Noisy Labeled Data Sree Ram Kamabattula V. Devarajan Babak Namazi G. Sankaranarayanan NoLa 8 2 0 24 Dec 2020
Semi-supervised novelty detection using ensembles with regularized disagreement A. Tifrea E. Stavarache Fanny Yang UQCV 29 6 0 10 Dec 2020
Robust Learning by Self-Transition for Handling Noisy Labels Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 13 40 0 08 Dec 2020
KNN-enhanced Deep Learning Against Noisy Labels Shuyu Kong You Li Jia Wang Amin Rezaei H. Zhou NoLa 11 5 0 08 Dec 2020
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization Qianyi Li H. Sompolinsky 16 69 0 07 Dec 2020
Coresets for Robust Training of Neural Networks against Noisy Labels Baharan Mirzasoleiman Kaidi Cao J. Leskovec NoLa 11 32 0 15 Nov 2020
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 24 158 0 09 Nov 2020
Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs Emma Rocheteau Doyoon Kim MedIm 8 3 0 06 Nov 2020
On Convergence and Generalization of Dropout Training Poorya Mianjy R. Arora 29 30 0 23 Oct 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 34 79 0 17 Sep 2020
Early Stopping in Deep Networks: Double Descent and How to Eliminate it Reinhard Heckel Fatih Yilmaz 26 43 0 20 Jul 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 24 960 0 16 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip H. S. Torr NoLa AAML 23 57 0 08 Jul 2020
Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu Jonathan Niles-Weed N. Razavian C. Fernandez‐Granda NoLa 8 553 0 30 Jun 2020