Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

13 September 2016

Papers citing "Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach"

50 / 265 papers shown

Title
Continual Learning on Noisy Data Streams via Self-Purified Replay C. Kim Jinseo Jeong Sang-chul Moon Gunhee Kim CLL 40 39 0 14 Oct 2021
Detecting Corrupted Labels Without Training a Model to Predict Zhaowei Zhu Zihao Dong Yang Liu NoLa 149 62 0 12 Oct 2021
Robustness and Reliability When Training With Noisy Labels Amanda Olmin Fredrik Lindsten OOD NoLa 18 14 0 07 Oct 2021
ARCA23K: An audio dataset for investigating open-set label noise Turab Iqbal Yin Cao A. Bailey Mark D. Plumbley Wenwu Wang 26 4 0 19 Sep 2021
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction Jiarun Liu Ruirui Li Chuan Sun OOD NoLa VLM 24 32 0 11 Sep 2021
Assessing the Quality of the Datasets by Identifying Mislabeled Samples Vaibhav Pulastya Gaurav Nuti Yash Kumar Atri Tanmoy Chakraborty NoLa 27 5 0 10 Sep 2021
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces Srinagesh Sharma Guoqing Zheng Ahmed Hassan Awadallah 27 1 0 09 Sep 2021
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training A. Gómez-Ríos Julián Luengo Francisco Herrera OOD NoLa 21 0 0 08 Sep 2021
Ghost Loss to Question the Reliability of Training Data A. Deliège A. Cioppa Marc Van Droogenbroeck UQCV 21 1 0 03 Sep 2021
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding Guoqing Zheng Giannis Karamanolakis Kai Shu Ahmed Hassan Awadallah SSL 21 1 0 28 Aug 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data Zhi-Fan Wu Tong Wei Jianwen Jiang Chaojie Mao Mingqian Tang Yu-Feng Li 11 80 0 25 Aug 2021
Cooperative Learning for Noisy Supervision Hao Wu Jiangchao Yao Ya-Qin Zhang Yanfeng Wang NoLa 14 2 0 11 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 28 55 0 05 Aug 2021
Co-learning: Learning from Noisy Labels with Self-supervision Cheng Tan Jun-Xiong Xia Lirong Wu Stan Z. Li NoLa 76 116 0 05 Aug 2021
Memorization in Deep Neural Networks: Does the Loss Function matter? Deep Patel P. Sastry TDI 27 8 0 21 Jul 2021
kNet: A Deep kNN Network To Handle Label Noise Itzik Mizrahi S. Avidan NoLa 21 0 0 20 Jul 2021
Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotations Darshan Gera B. S 13 7 0 10 Jul 2021
Mitigating Memorization in Sample Selection for Learning with Noisy Labels Kyeongbo Kong Junggi Lee Youngchul Kwak Young-Rae Cho Seong-Eun Kim Woo‐Jin Song NoLa 18 0 0 08 Jul 2021
Label noise in segmentation networks : mitigation must deal with bias Eugene Vorontsov Samuel Kadoury NoLa 31 19 0 05 Jul 2021
Adaptive Sample Selection for Robust Learning under Label Noise Deep Patel P. Sastry OOD NoLa 28 29 0 29 Jun 2021
Distilling effective supervision for robust medical image segmentation with noisy labels Jialin Shi Ji Wu NoLa 19 32 0 21 Jun 2021
Being Properly Improper Tyler Sypherd Richard Nock Lalitha Sankar FaML 39 10 0 18 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 30 44 0 17 Jun 2021
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs Enyan Dai Charu C. Aggarwal Suhang Wang NoLa 27 114 0 08 Jun 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei Hangyu Liu Tongliang Liu Gang Niu Masashi Sugiyama Yang Liu NoLa 32 69 0 08 Jun 2021
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise Ming-Kun Xie Sheng-Jun Huang NoLa 24 25 0 16 May 2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels Erik Englesson Hossein Azizpour NoLa 34 103 0 10 May 2021
Self-paced Resistance Learning against Overfitting on Noisy Labels Xiaoshuang Shi Zhenhua Guo Fuyong Xing Yun Liang Xiaofeng Zhu NoLa 21 20 0 07 May 2021
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning Yuyang Gao Giorgio Ascoli Liang Zhao 19 4 0 05 May 2021
Boosting Co-teaching with Compression Regularization for Label Noise Yingyi Chen Xin Shen S. Hu Johan A. K. Suykens NoLa 45 45 0 28 Apr 2021
Contrastive Learning Improves Model Robustness Under Label Noise Aritra Ghosh Andrew S. Lan NoLa 21 58 0 19 Apr 2021
Self-Training with Weak Supervision Giannis Karamanolakis Subhabrata Mukherjee Guoqing Zheng Ahmed Hassan Awadallah NoLa 16 86 0 12 Apr 2021
Learning from Noisy Labels via Dynamic Loss Thresholding Hao Yang Youzhi Jin Zi-Hua Li Deng-Bao Wang Lei Miao Xin Geng Min-Ling Zhang NoLa AI4CE 32 6 0 01 Apr 2021
Collaborative Label Correction via Entropy Thresholding Hao Wu Jiangchao Yao Jiajie Wang Yinru Chen Ya-Qin Zhang Yanfeng Wang NoLa 22 4 0 31 Mar 2021
Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation Waqar Ahmed Pietro Morerio Vittorio Murino 16 4 0 29 Mar 2021
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation Chen Li G. Lee OOD 11 81 0 27 Mar 2021
Jo-SRC: A Contrastive Approach for Combating Noisy Labels Yazhou Yao Zeren Sun Chuanyi Zhang Fumin Shen Qi Wu Jian Zhang Zhenmin Tang NoLa 33 133 0 24 Mar 2021
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels Purvi Goel Li Chen NoLa 33 15 0 22 Mar 2021
Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification Youngwoo Kim Cheng Li Bingyang Ye A. Tahmasebi J. Aslam NoLa 19 1 0 13 Mar 2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment F. Cordeiro Ragav Sachdeva Vasileios Belagiannis Ian Reid G. Carneiro NoLa 13 77 0 06 Mar 2021
Unified Robust Training for Graph NeuralNetworks against Label Noise Yayong Li Jie Yin Ling-Hao Chen NoLa 26 29 0 05 Mar 2021
DST: Data Selection and joint Training for Learning with Noisy Labels Yi Wei Xue Mei Xin Liu Pengxiang Xu NoLa 27 3 0 01 Mar 2021
Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits Octavian Suciu Connor Nelson Zhuo Lyu Tiffany Bao Tudor Dumitras 6 36 0 15 Feb 2021
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Zhaowei Zhu Yiwen Song Yang Liu NoLa 13 91 0 10 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan S. Kankanhalli Masashi Sugiyama AAML 27 27 0 06 Feb 2021
Analysing the Noise Model Error for Realistic Noisy Label Data Michael A. Hedderich D. Zhu Dietrich Klakow NoLa 29 19 0 24 Jan 2021
Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification Yongxing Dai Jun Liu Yan Bai Zekun Tong Ling-yu Duan 26 77 0 26 Dec 2020
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification A. Aksoy Mahdyar Ravanbakhsh Begüm Demir 29 24 0 19 Dec 2020
From Weakly Supervised Learning to Biquality Learning: an Introduction Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols A. Ouorou 14 21 0 16 Dec 2020
Attentional-Biased Stochastic Gradient Descent Q. Qi Yi Tian Xu R. L. Jin W. Yin Tianbao Yang ODL 26 12 0 13 Dec 2020