You can't handle the (dirty) truth: Data-centric insights improve
pseudo-labeling

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling

19 June 2024

Mihaela van der Schaar

Papers citing "You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling"

7 / 7 papers shown

Title
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI Nabeel Seedat F. Imrie M. Schaar 21 8 0 07 Mar 2024
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Yidong Wang Hao Chen Qiang Heng Wenxin Hou Yue Fan ... Marios Savvides T. Shinozaki Bhiksha Raj Bernt Schiele Xing Xie 185 258 0 15 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 234 862 0 15 Oct 2021
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training Kai Sheng Tai Peter Bailis Gregory Valiant OT 45 43 0 17 Feb 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Y. S. Rawat M. Shah 229 509 0 15 Jan 2021
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 208 107 0 26 Aug 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016