Learning Deep Neural Networks under Agnostic Corrupted Supervision

Learning Deep Neural Networks under Agnostic Corrupted Supervision

12 February 2021

Papers citing "Learning Deep Neural Networks under Agnostic Corrupted Supervision"

11 / 11 papers shown

Title
FedNoisy: Federated Noisy Label Learning Benchmark Siqi Liang Jintao Huang Junyuan Hong Dun Zeng Jiayu Zhou Zenglin Xu FedML 101 7 0 20 Jun 2023
Robust Collaborative Learning with Noisy Labels Mengying Sun Jing Xing Bin Chen Jiayu Zhou NoLa 21 3 0 26 Dec 2020
Error-Bounded Correction of Noisy Labels Songzhu Zheng Pengxiang Wu A. Goswami Mayank Goswami Dimitris N. Metaxas Chao Chen NoLa 58 119 0 19 Nov 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 345 514 0 05 Mar 2020
Big Transfer (BiT): General Visual Representation Learning Alexander Kolesnikov Lucas Beyer Xiaohua Zhai J. Puigcerver Jessica Yung Sylvain Gelly N. Houlsby MQ 286 1,205 0 24 Dec 2019
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels Kun Yi Jianxin Wu NoLa 68 417 0 19 Mar 2019
How does Disagreement Help Generalization against Label Corruption? Xingrui Yu Bo Han Jiangchao Yao Gang Niu Ivor W. Tsang Masashi Sugiyama NoLa 65 783 0 14 Jan 2019
Sever: A Robust Meta-Algorithm for Stochastic Optimization Ilias Diakonikolas Gautam Kamath D. Kane Jerry Li Jacob Steinhardt Alistair Stewart 62 289 0 07 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 98 1,453 0 14 Dec 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 339 4,626 0 10 Nov 2016
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt Nicolas Le Roux Francis R. Bach 205 582 0 12 Sep 2011

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