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Consistency-Guided Temperature Scaling Using Style and Content
  Information for Out-of-Domain Calibration

Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration

22 February 2024
Wonjeong Choi
Jun-Gyu Park
Dong-Jun Han
Younghyun Park
Jaekyun Moon
ArXivPDFHTML

Papers citing "Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration"

3 / 3 papers shown
Title
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
Seong-Hyeon Hwang
Minsu Kim
Steven Euijong Whang
34
0
0
28 Mar 2025
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
252
9,134
0
06 Jun 2015
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
228
31,244
0
16 Jan 2013
1