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Is Out-of-Distribution Detection Learnable?

Is Out-of-Distribution Detection Learnable?

26 October 2022
Zhen Fang
Yixuan Li
Jie Lu
Jiahua Dong
Bo Han
Feng Liu
    OODD
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Papers citing "Is Out-of-Distribution Detection Learnable?"

14 / 64 papers shown
Title
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
757
0
22 Oct 2018
Open Category Detection with PAC Guarantees
Open Category Detection with PAC Guarantees
Si Liu
Risheek Garrepalli
Thomas G. Dietterich
Alan Fern
Dan Hendrycks
53
84
0
01 Aug 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and
  Adversarial Attacks
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee
Kibok Lee
Honglak Lee
Jinwoo Shin
OODD
185
2,050
0
10 Jul 2018
Glow: Generative Flow with Invertible 1x1 Convolutions
Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma
Prafulla Dhariwal
BDL
DRL
292
3,129
0
09 Jul 2018
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi
Ranya Almohsen
Donald Adjeroh
Gianfranco Doretto
UQCV
42
322
0
06 Jul 2018
Binary Classification from Positive-Confidence Data
Binary Classification from Positive-Confidence Data
Takashi Ishida
Gang Niu
Masashi Sugiyama
55
57
0
19 Oct 2017
Enhancing The Reliability of Out-of-distribution Image Detection in
  Neural Networks
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang
Yixuan Li
R. Srikant
UQCV
OODD
168
2,069
0
08 Jun 2017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise
  linear neural networks
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett
Nick Harvey
Christopher Liaw
Abbas Mehrabian
203
432
0
08 Mar 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo
Gang Niu
M. C. D. Plessis
Masashi Sugiyama
69
476
0
02 Mar 2017
Depth-Width Tradeoffs in Approximating Natural Functions with Neural
  Networks
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Itay Safran
Ohad Shamir
80
174
0
31 Oct 2016
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
155
3,452
0
07 Oct 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN
3DV
772
36,794
0
25 Aug 2016
Towards Open Set Deep Networks
Towards Open Set Deep Networks
Abhijit Bendale
Terrance Boult
BDL
EDL
104
1,426
0
19 Nov 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.8K
150,039
0
22 Dec 2014
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