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Understanding out-of-distribution accuracies through quantifying
  difficulty of test samples

Understanding out-of-distribution accuracies through quantifying difficulty of test samples

28 March 2022
Berfin Simsek
Melissa Hall
Levent Sagun
ArXivPDFHTML

Papers citing "Understanding out-of-distribution accuracies through quantifying difficulty of test samples"

5 / 5 papers shown
Title
What classifiers know what they don't?
What classifiers know what they don't?
Mohamed Ishmael Belghazi
David Lopez-Paz
15
6
0
13 Jul 2021
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
2,592
0
04 May 2021
Estimating Example Difficulty Using Variance of Gradients
Estimating Example Difficulty Using Variance of Gradients
Chirag Agarwal
Daniel D'souza
Sara Hooker
190
105
0
26 Aug 2020
An Investigation of Why Overparameterization Exacerbates Spurious
  Correlations
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Shiori Sagawa
Aditi Raghunathan
Pang Wei Koh
Percy Liang
144
369
0
09 May 2020
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
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