EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning

EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning

13 June 2022

Yin Li

Papers citing "EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning"

5 / 5 papers shown

Title
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 231 862 0 15 Oct 2021
Graph-based Semi-supervised Learning: A Comprehensive Review Zixing Song Xiangli Yang Zenglin Xu Irwin King 81 191 0 26 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 220 508 0 15 Jan 2021
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015