Learning From Noisy Singly-labeled Data

13 December 2017

Papers citing "Learning From Noisy Singly-labeled Data"

37 / 37 papers shown

Title
Theoretical Analysis of Weak-to-Strong Generalization Hunter Lang David Sontag Aravindan Vijayaraghavan 35 21 0 25 May 2024
Multi-annotator Deep Learning: A Probabilistic Framework for Classification M. Herde Denis Huseljic Bernhard Sick 40 9 0 05 Apr 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 43 9 0 30 Jan 2023
Sources of Noise in Dialogue and How to Deal with Them Derek Chen Zhou Yu 34 2 0 06 Dec 2022
CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators Hui Wen Goh Ulyana Tkachenko Jonas W. Mueller 19 10 0 13 Oct 2022
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion Zhengqi Gao Fan-Keng Sun Ming-Hsuan Yang Sucheng Ren Zikai Xiong Marc Engeler Antonio Burazer L. Wildling Lucani E. Daniel Duane S. Boning NoLa 41 15 0 22 Jul 2022
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection Shaoshen Wang Yanbin Liu Ling Chen Chengqi Zhang 24 0 0 29 May 2022
A Survey on Programmatic Weak Supervision Jieyu Zhang Cheng-Yu Hsieh Yue Yu Chao Zhang Alexander Ratner 36 91 0 11 Feb 2022
Data Consistency for Weakly Supervised Learning Chidubem Arachie Bert Huang NoLa 24 2 0 08 Feb 2022
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits Doyeon Kim Jeonghwa Lee Hye Won Chung 58 3 0 19 Nov 2021
Clean or Annotate: How to Spend a Limited Data Collection Budget Derek Chen Zhou Yu Samuel R. Bowman 41 13 0 15 Oct 2021
Creating Training Sets via Weak Indirect Supervision Jieyu Zhang Bohan Wang Xiangchen Song Yujing Wang Yaming Yang Jing Bai Alexander Ratner OffRL 54 17 0 07 Oct 2021
kNet: A Deep kNN Network To Handle Label Noise Itzik Mizrahi S. Avidan NoLa 21 0 0 20 Jul 2021
End-to-End Weak Supervision Salva Rühling Cachay Benedikt Boecking A. Dubrawski NoLa 33 40 0 05 Jul 2021
Label noise in segmentation networks : mitigation must deal with bias Eugene Vorontsov Samuel Kadoury NoLa 36 19 0 05 Jul 2021
Learning from Multiple Annotators by Incorporating Instance Features Jingzheng Li Hailong Sun Jiyi Li Zhijun Chen Renshuai Tao Yufei Ge NoLa 29 5 0 29 Jun 2021
Cost-Accuracy Aware Adaptive Labeling for Active Learning Ruijiang Gao M. Saar-Tsechansky HAI 16 19 0 24 May 2021
Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features Q. D. Cao Youngjun Choe 21 6 0 15 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 24 159 0 09 Nov 2020
Constrained Labeling for Weakly Supervised Learning Chidubem Arachie Bert Huang 36 17 0 15 Sep 2020
Ontology-driven weak supervision for clinical entity classification in electronic health records Jason Alan Fries E. Steinberg S. Khattar Scott L. Fleming J. Posada A. Callahan N. Shah 26 72 0 05 Aug 2020
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID Yang Long Guisong Xia Shengyang Li Wen Yang M. Yang Xiaoxiang Zhu Liangpei Zhang Deren Li 23 21 0 22 Jun 2020
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods Daniel Y. Fu Mayee F. Chen Frederic Sala Sarah Hooper Kayvon Fatahalian Christopher Ré OffRL 34 111 0 27 Feb 2020
Strength from Weakness: Fast Learning Using Weak Supervision Joshua Robinson Stefanie Jegelka S. Sra 43 32 0 19 Feb 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible Antonin Berthon Bo Han Gang Niu Tongliang Liu Masashi Sugiyama NoLa 46 104 0 11 Jan 2020
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis Davood Karimi Haoran Dou Simon K. Warfield Ali Gholipour NoLa 28 536 0 05 Dec 2019
Confident Learning: Estimating Uncertainty in Dataset Labels Curtis G. Northcutt Lu Jiang Isaac L. Chuang NoLa 66 676 0 31 Oct 2019
Multi-Resolution Weak Supervision for Sequential Data Frederic Sala P. Varma Jason Alan Fries Daniel Y. Fu Shiori Sagawa ... A. Ramamoorthy K. Xiao Kayvon Fatahalian J. Priest Christopher Ré NoLa 22 28 0 21 Oct 2019
Noisy Batch Active Learning with Deterministic Annealing Gaurav Gupta Anit Kumar Sahu Wan-Yi Lin NoLa 37 6 0 27 Sep 2019
VILD: Variational Imitation Learning with Diverse-quality Demonstrations Voot Tangkaratt Bo Han Mohammad Emtiyaz Khan Masashi Sugiyama 25 20 0 15 Sep 2019
Learning with Noisy Labels for Sentence-level Sentiment Classification Hao Wang Bing-Quan Liu Chaozhuo Li Yan Yang Tianrui Li NoLa 29 26 0 31 Aug 2019
NLNL: Negative Learning for Noisy Labels Youngdong Kim Junho Yim Juseung Yun Junmo Kim NoLa 17 265 0 19 Aug 2019
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 66 172 0 24 May 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 57 351 0 27 Mar 2019
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion Ryutaro Tanno A. Saeedi S. Sankaranarayanan Daniel C. Alexander N. Silberman NoLa 27 228 0 10 Feb 2019
Robustness of Conditional GANs to Noisy Labels Kerry J. Halupka A. Khetan Zinan Lin Stephen Moore NoLa 24 79 0 08 Nov 2018