NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning

31 January 2023

Papers citing "NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning"

9 / 9 papers shown

Title
NP-Match: When Neural Processes meet Semi-Supervised Learning Jianfeng Wang Thomas Lukasiewicz Daniela Massiceti Xiaolin Hu Vladimir Pavlovic A. Neophytou BDL 68 41 0 03 Jul 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 255 863 0 15 Oct 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Yogesh S Rawat M. Shah 241 509 0 15 Jan 2021
Improving model calibration with accuracy versus uncertainty optimization R. Krishnan Omesh Tickoo UQCV 188 157 0 14 Dec 2020
Meta Pseudo Labels Hieu H. Pham Zihang Dai Qizhe Xie Minh-Thang Luong Quoc V. Le VLM 262 656 0 23 Mar 2020
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 267 1,275 0 06 Mar 2017
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 315 36,381 0 25 Aug 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015
Statistical exponential families: A digest with flash cards Frank Nielsen Vincent Garcia 88 183 0 25 Nov 2009