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Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

17 June 2025
Conrad Orglmeister
Erik Bochinski
Volker Eiselein
Elvira Fleig
ArXiv (abs)PDFHTML

Papers citing "Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection"

7 / 7 papers shown
Title
Adversarial Reciprocal Points Learning for Open Set Recognition
Adversarial Reciprocal Points Learning for Open Set Recognition
Guangyao Chen
Peixi Peng
Xiangqian Wang
Yonghong Tian
114
293
0
01 Mar 2021
Likelihood Regret: An Out-of-Distribution Detection Score For
  Variational Auto-encoder
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Zhisheng Xiao
Qing Yan
Y. Amit
OODD
148
195
0
06 Mar 2020
Do Deep Generative Models Know What They Don't Know?
Do Deep Generative Models Know What They Don't Know?
Eric T. Nalisnick
Akihiro Matsukawa
Yee Whye Teh
Dilan Görür
Balaji Lakshminarayanan
OOD
66
759
0
22 Oct 2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Y. Zhang
Phillip Isola
Alexei A. Efros
Eli Shechtman
Oliver Wang
EGVM
377
11,877
0
11 Jan 2018
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
166
3,468
0
07 Oct 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
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
UQCVBDL
831
9,345
0
06 Jun 2015
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