Confident Sinkhorn Allocation for Pseudo-Labeling

13 June 2022

Papers citing "Confident Sinkhorn Allocation for Pseudo-Labeling"

8 / 8 papers shown

Title
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling Nabeel Seedat Nicolas Huynh F. Imrie Mihaela van der Schaar 31 0 0 19 Jun 2024
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels Wanxing Chang Ye-ling Shi Jingya Wang OT 33 12 0 11 Dec 2023
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 215 861 0 15 Oct 2021
Label Propagation Through Optimal Transport Mourad El Hamri Younès Bennani Issam Falih OT 13 4 0 01 Oct 2021
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training Kai Sheng Tai Peter Bailis Gregory Valiant OT 38 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 203 506 0 15 Jan 2021
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote A. Masegosa S. Lorenzen Christian Igel Yevgeny Seldin 24 40 0 01 Jul 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,109 0 06 Jun 2015