Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

14 February 2018

Papers citing "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise"

35 / 285 papers shown

Title
Anchor Loss: Modulating Loss Scale based on Prediction Difficulty Serim Ryou Seong-Gyun Jeong Pietro Perona 19 43 0 24 Sep 2019
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels Yucen Luo Jun Zhu Tomas Pfister NoLa 18 6 0 20 Sep 2019
L_DMI: An Information-theoretic Noise-robust Loss Function Yilun Xu Peng Cao Yuqing Kong Yizhou Wang NoLa 19 57 0 08 Sep 2019
NLNL: Negative Learning for Noisy Labels Youngdong Kim Junho Yim Juseung Yun Junmo Kim NoLa 17 265 0 19 Aug 2019
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning Eric Arazo Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness 34 818 0 08 Aug 2019
Deep Self-Learning From Noisy Labels Jiangfan Han Ping Luo Xiaogang Wang NoLa 30 278 0 06 Aug 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath D. Song OOD SSL 10 936 0 28 Jun 2019
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference Martin Schmitt Hinrich Schütze 19 14 0 04 Jun 2019
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation Wei Wang Isaac Caswell Ciprian Chelba 28 57 0 03 Jun 2019
Uncertainty Based Detection and Relabeling of Noisy Image Labels Jan M. Köhler Maximilian Autenrieth William H. Beluch NoLa 33 28 0 29 May 2019
Derivative Manipulation for General Example Weighting Xinshao Wang Elyor Kodirov Yang Hua N. Robertson NoLa 13 1 0 27 May 2019
Combating Label Noise in Deep Learning Using Abstention S. Thulasidasan Tanmoy Bhattacharya J. Bilmes Gopinath Chennupati J. Mohd-Yusof NoLa 22 177 0 27 May 2019
Stability Properties of Graph Neural Networks Fernando Gama Joan Bruna Alejandro Ribeiro 30 226 0 11 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 44 602 0 25 Apr 2019
Wasserstein Adversarial Regularization (WAR) on label noise Kilian Fatras B. Bushan Sylvain Lobry Rémi Flamary D. Tuia Nicolas Courty 19 24 0 08 Apr 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 12 3,353 0 28 Mar 2019
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling Debjit Paul Mittul Singh Michael A. Hedderich Dietrich Klakow NoLa 16 17 0 28 Mar 2019
Noise-Tolerant Paradigm for Training Face Recognition CNNs Wei Hu Yangyu Huang Fan Zhang Ruirui Li NoLa CVBM 24 62 0 25 Mar 2019
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu Qi Xie Lixuan Yi Qian Zhao Sanping Zhou Zongben Xu Deyu Meng NoLa 12 4 0 20 Feb 2019
Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee Sukmin Yun Kibok Lee Honglak Lee Bo-wen Li Jinwoo Shin NoLa 33 134 0 31 Jan 2019
Robust Learning from Untrusted Sources Nikola Konstantinov Christoph H. Lampert FedML OOD 11 71 0 29 Jan 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 34 720 0 28 Jan 2019
How does Disagreement Help Generalization against Label Corruption? Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W. Tsang Masashi Sugiyama NoLa 6 769 0 14 Jan 2019
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels Sabri Boughorbel Fethi Jarray Neethu Venugopal Haithum Elhadi NoLa 11 11 0 24 Nov 2018
Limited Gradient Descent: Learning With Noisy Labels Yi Sun Yan Tian Yiping Xu Jianxiang Li NoLa 35 13 0 20 Nov 2018
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images B. Damodaran Rémi Flamary Vivien Seguy Nicolas Courty NoLa 29 39 0 02 Oct 2018
Reinforcement Learning with Perturbed Rewards Jingkang Wang Yang Liu Bo-wen Li NoLa 25 127 0 02 Oct 2018
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust Bo Han Gang Niu Xingrui Yu Quanming Yao Miao Xu Ivor Tsang Masashi Sugiyama NoLa 17 7 0 28 Sep 2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations Dan Hendrycks Thomas G. Dietterich OOD 14 197 0 04 Jul 2018
Masking: A New Perspective of Noisy Supervision Bo Han Jiangchao Yao Gang Niu Mingyuan Zhou Ivor Tsang Ya Zhang Masashi Sugiyama NoLa 16 253 0 21 May 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 34 2,551 0 20 May 2018
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks Sungjoon Choi Sanghoon Hong Kyungjae Lee Sungbin Lim OOD 27 8 0 16 May 2018
Learning to Reweight Examples for Robust Deep Learning Mengye Ren Wenyuan Zeng Binh Yang R. Urtasun OOD NoLa 69 1,411 0 24 Mar 2018
Learning with Bounded Instance- and Label-dependent Label Noise Jiacheng Cheng Tongliang Liu K. Ramamohanarao Dacheng Tao NoLa 43 147 0 12 Sep 2017
Learning from Binary Labels with Instance-Dependent Corruption A. Menon Brendan van Rooyen Nagarajan Natarajan NoLa 36 41 0 03 May 2016