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SequenceMatch: Revisiting the design of weak-strong augmentations for
  Semi-supervised learning

SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning

24 October 2023
Khanh-Binh Nguyen
ArXivPDFHTML

Papers citing "SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning"

5 / 5 papers shown
Title
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
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
175
251
0
15 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
  Labeling
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang
Yidong Wang
Wenxin Hou
Hao Wu
Jindong Wang
Manabu Okumura
T. Shinozaki
AAML
213
848
0
15 Oct 2021
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Jiawei Ren
Cunjun Yu
Shunan Sheng
Xiao Ma
Haiyu Zhao
Shuai Yi
Hongsheng Li
157
541
0
21 Jul 2020
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
Improved Baselines with Momentum Contrastive Learning
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
238
3,359
0
09 Mar 2020
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