Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy

2 November 2023

Papers citing "Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy"

8 / 8 papers shown

Title
Geometric Median (GM) Matching for Robust Data Pruning Anish Acharya Inderjit S Dhillon Sujay Sanghavi AAML 59 0 0 20 Jan 2025
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models Zachary Ankner Cody Blakeney Kartik K. Sreenivasan Max Marion Matthew L. Leavitt Mansheej Paul 30 23 0 30 May 2024
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning Xin Zhang Jiawei Du Yunsong Li Weiying Xie Joey Tianyi Zhou 25 6 0 22 Nov 2023
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning Dongmin Park Yooju Shin Jihwan Bang Youngjune Lee Hwanjun Song Jae-Gil Lee 52 24 0 13 Oct 2022
Consistency Regularization Can Improve Robustness to Label Noise Erik Englesson Hossein Azizpour NoLa 48 19 0 04 Oct 2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training Krishnateja Killamsetty D. Sivasubramanian Ganesh Ramakrishnan A. De Rishabh K. Iyer OOD 78 184 0 27 Feb 2021
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 488 0 05 Mar 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 220 3,054 0 23 Jan 2020