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Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for
  Semi-Supervised Text Recognition

Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition

31 August 2022
Gaurav Patel
J. Allebach
Qiang Qiu
    UQLM
ArXivPDFHTML

Papers citing "Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition"

5 / 5 papers shown
Title
Entropy Heat-Mapping: Localizing GPT-Based OCR Errors with Sliding-Window Shannon Analysis
Entropy Heat-Mapping: Localizing GPT-Based OCR Errors with Sliding-Window Shannon Analysis
Alexei Kaltchenko
51
0
0
30 Apr 2025
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label
  Selection Framework for Semi-Supervised Learning
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
238
509
0
15 Jan 2021
Mean teachers are better role models: Weight-averaged consistency
  targets improve semi-supervised deep learning results
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
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in
  Natural Images
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
Andreas Veit
Tomas Matera
Lukás Neumann
Jirí Matas
Serge J. Belongie
188
515
0
26 Jan 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
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