Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

13 May 2019

Papers citing "Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels"

24 / 74 papers shown

Title
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
Co-learning: Learning from Noisy Labels with Self-supervision Cheng Tan Jun Xia Lirong Wu Stan Z. Li NoLa 76 116 0 05 Aug 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
Federated Noisy Client Learning Huazhu Fu Li Li Bo Han Chengzhong Xu Ling Shao FedML 28 26 0 24 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 33 44 0 17 Jun 2021
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise Andrea Castellani Sebastian Schmitt Barbara Hammer NoLa 30 18 0 01 May 2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment F. Cordeiro Ragav Sachdeva Vasileios Belagiannis Ian Reid G. Carneiro NoLa 19 77 0 06 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
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 42 144 0 11 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
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 40 122 0 10 Dec 2020
A Topological Filter for Learning with Label Noise Pengxiang Wu Songzhu Zheng Mayank Goswami Dimitris N. Metaxas Chao Chen NoLa 30 112 0 09 Dec 2020
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 103 34 0 08 Dec 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
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret Ariel Kleiner H. Mobahi Behnam Neyshabur AAML 116 1,278 0 03 Oct 2020
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics Swabha Swayamdipta Roy Schwartz Nicholas Lourie Yizhong Wang Hannaneh Hajishirzi Noah A. Smith Yejin Choi 44 429 0 22 Sep 2020
MoPro: Webly Supervised Learning with Momentum Prototypes Junnan Li Caiming Xiong Guosheng Lin 26 94 0 17 Sep 2020
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels Jun Shu Qian Zhao Zengben Xu Deyu Meng NoLa 31 29 0 10 Jun 2020
Towards Noise-resistant Object Detection with Noisy Annotations Junnan Li Caiming Xiong R. Socher Guosheng Lin ObjD NoLa 62 28 0 03 Mar 2020
Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs Han Yang Xiao Yan XINYAN DAI Yongqiang Chen James Cheng 13 36 0 18 Feb 2020
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis Davood Karimi Haoran Dou Simon K. Warfield Ali Gholipour NoLa 24 535 0 05 Dec 2019
Confident Learning: Estimating Uncertainty in Dataset Labels Curtis G. Northcutt Lu Jiang Isaac L. Chuang NoLa 38 674 0 31 Oct 2019
Generative Imputation and Stochastic Prediction Mohammad Kachuee Kimmo Karkkainen Orpaz Goldstein Sajad Darabi Majid Sarrafzadeh 22 23 0 22 May 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