Learning from Binary Labels with Instance-Dependent Corruption

3 May 2016

Papers citing "Learning from Binary Labels with Instance-Dependent Corruption"

22 / 22 papers shown

Title
Meta Self-Refinement for Robust Learning with Weak Supervision D. Zhu Xiaoyu Shen Michael A. Hedderich Dietrich Klakow NoLa 34 7 0 15 May 2022
The Weak Supervision Landscape Rafael Poyiadzi Daniel Bacaicoa-Barber Jesús Cid-Sueiro Miquel Perelló Nieto Peter A. Flach Raúl Santos-Rodríguez 19 5 0 30 Mar 2022
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection Seong Min Kye Kwanghee Choi Joonyoung Yi Buru Chang NoLa 35 15 0 29 Nov 2021
Binary classification with corrupted labels Yonghoon Lee Rina Foygel Barber 34 9 0 16 Jun 2021
Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits Octavian Suciu Connor Nelson Zhuo Lyu Tiffany Bao Tudor Dumitras 8 36 0 15 Feb 2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model Qizhou Wang Bo Han Tongliang Liu Gang Niu Jian Yang Chen Gong NoLa 25 26 0 14 Jan 2021
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 24 159 0 09 Nov 2020
Importance Reweighting for Biquality Learning Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols NoLa 27 6 0 19 Oct 2020
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization Kaidi Cao Yining Chen Junwei Lu Nikos Arechiga Adrien Gaidon Tengyu Ma 28 68 0 29 Jun 2020
Learning from Noisy Similar and Dissimilar Data Soham Dan Han Bao Masashi Sugiyama NoLa 8 7 0 03 Feb 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible Antonin Berthon Bo Han Gang Niu Tongliang Liu Masashi Sugiyama NoLa 40 104 0 11 Jan 2020
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition Adrien Dufraux Emmanuel Vincent Awni Y. Hannun Armelle Brun Matthijs Douze 6 9 0 16 Oct 2019
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels Yucen Luo Jun Zhu Tomas Pfister NoLa 23 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
Learning with Bad Training Data via Iterative Trimmed Loss Minimization Yanyao Shen Sujay Sanghavi FedML 17 4 0 28 Oct 2018
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data Nan Lu Gang Niu A. Menon Masashi Sugiyama MQ 30 86 0 31 Aug 2018
Classification with imperfect training labels T. Cannings Yingying Fan R. Samworth 17 45 0 29 May 2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise Dan Hendrycks Mantas Mazeika Duncan Wilson Kevin Gimpel NoLa 68 547 0 14 Feb 2018
Decontamination of Mutual Contamination Models Julian Katz-Samuels Gilles Blanchard Clayton Scott 69 24 0 30 Sep 2017
Learning with Bounded Instance- and Label-dependent Label Noise Jiacheng Cheng Tongliang Liu K. Ramamohanarao Dacheng Tao NoLa 43 147 0 12 Sep 2017
Decoupling "when to update" from "how to update" Eran Malach Shai Shalev-Shwartz NoLa 20 563 0 08 Jun 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Lizhen Qu NoLa 51 1,435 0 13 Sep 2016