Identifying Mislabeled Instances in Classification Datasets

11 December 2019

Papers citing "Identifying Mislabeled Instances in Classification Datasets"

12 / 12 papers shown

Title
Why is SAM Robust to Label Noise? Christina Baek Zico Kolter Aditi Raghunathan NoLa AAML 43 9 0 06 May 2024
On Evaluation of Document Classification using RVL-CDIP Stefan Larson Gordon Lim Kevin Leach 39 3 0 21 Jun 2023
Detecting Errors in a Numerical Response via any Regression Model Hang Zhou Jonas W. Mueller Mayank Kumar Jane-ling Wang Jing-Sheng Lei 36 0 0 26 May 2023
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method Siwei Wang S. Palmer 35 2 0 19 May 2023
Identifying Incorrect Annotations in Multi-Label Classification Data Aditya Thyagarajan Elías Snorrason Curtis G. Northcutt Jonas W. Mueller 37 10 0 25 Nov 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness Chester Holtz Tsui-Wei Weng Gal Mishne OOD 35 4 0 20 Oct 2022
Detecting Label Errors in Token Classification Data Wei-Chen Wang Jonas W. Mueller 29 13 0 08 Oct 2022
Efficient Adversarial Training With Data Pruning Maximilian Kaufmann Yiren Zhao Ilia Shumailov Robert D. Mullins Nicolas Papernot AAML 44 7 0 01 Jul 2022
Detecting Label Errors by using Pre-Trained Language Models Derek Chong Jenny Hong Christopher D. Manning NoLa 55 21 0 25 May 2022
Training Dynamic based data filtering may not work for NLP datasets Arka Talukdar Monika Dagar Prachi Gupta Varun G. Menon NoLa 48 3 0 19 Sep 2021
Assessing the Quality of the Datasets by Identifying Mislabeled Samples Vaibhav Pulastya Gaurav Nuti Yash Kumar Atri Tanmoy Chakraborty NoLa 35 5 0 10 Sep 2021
Prevalence of Neural Collapse during the terminal phase of deep learning training Vardan Papyan Xuemei Han D. Donoho 35 551 0 18 Aug 2020