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What Can We Learn From The Selective Prediction And Uncertainty
  Estimation Performance Of 523 Imagenet Classifiers

What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers

23 February 2023
Ido Galil
Mohammed Dabbah
Ran El-Yaniv
    UQCV
ArXivPDFHTML

Papers citing "What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers"

8 / 8 papers shown
Title
Confidence Calibration of Classifiers with Many Classes
Confidence Calibration of Classifiers with Many Classes
Adrien LeCoz
Stéphane Herbin
Faouzi Adjed
UQCV
33
1
0
05 Nov 2024
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
Arthur Pignet
Chiara Regniez
John Klein
57
1
0
30 Oct 2024
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
Wataru Hashimoto
Hidetaka Kamigaito
Taro Watanabe
52
0
0
02 Jul 2024
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications
Jordy Van Landeghem
Subhajit Maity
Ayan Banerjee
Matthew Blaschko
Marie-Francine Moens
Josep Lladós
Sanket Biswas
41
2
0
12 Jun 2024
Which models are innately best at uncertainty estimation?
Which models are innately best at uncertainty estimation?
Ido Galil
Mohammed Dabbah
Ran El-Yaniv
UQCV
12
5
0
05 Jun 2022
ImageNet-21K Pretraining for the Masses
ImageNet-21K Pretraining for the Masses
T. Ridnik
Emanuel Ben-Baruch
Asaf Noy
Lihi Zelnik-Manor
SSeg
VLM
CLIP
166
684
0
22 Apr 2021
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
5,652
0
05 Dec 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
247
9,109
0
06 Jun 2015
1