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AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

25 January 2022
Jiwon Kim
Kwang-seok Ryoo
Gyuseong Lee
Seokju Cho
Junyoung Seo
Daehwan Kim
Hansang Cho
Seung Wook Kim
ArXivPDFHTML

Papers citing "AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning"

4 / 4 papers shown
Title
MLP-Mixer: An all-MLP Architecture for Vision
MLP-Mixer: An all-MLP Architecture for Vision
Ilya O. Tolstikhin
N. Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
...
Andreas Steiner
Daniel Keysers
Jakob Uszkoreit
Mario Lucic
Alexey Dosovitskiy
239
2,554
0
04 May 2021
Meta Pseudo Labels
Meta Pseudo Labels
Hieu H. Pham
Zihang Dai
Qizhe Xie
Minh-Thang Luong
Quoc V. Le
VLM
245
648
0
23 Mar 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should
  Average
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun
Marc Finzi
Pavel Izmailov
A. Wilson
191
243
0
14 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
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