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$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of
  Deep Representations

ppp-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations

25 July 2022
Adam Dziedzic
Stephan Rabanser
Mohammad Yaghini
Armin Ale
Murat A. Erdogdu
Nicolas Papernot
    AAML
ArXivPDFHTML

Papers citing "$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations"

4 / 4 papers shown
Title
Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach
Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach
Nicolas Atienza
Christophe Labreuche
Johanne Cohen
Michele Sebag
OODD
AAML
141
0
0
20 Jan 2025
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
276
5,660
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
285
9,136
0
06 Jun 2015
Combining p-values via averaging
Combining p-values via averaging
V. Vovk
Ruodu Wang
FedML
65
205
0
20 Dec 2012
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