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Interpreting Rate-Distortion of Variational Autoencoder and Using Model
  Uncertainty for Anomaly Detection
v1v2 (latest)

Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection

5 May 2020
Seonho Park
George Adosoglou
P. Pardalos
    DRLUQCV
ArXiv (abs)PDFHTML

Papers citing "Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection"

32 / 32 papers shown
Title
Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection
Unsupervised Anomaly Detection Using Diffusion Trend Analysis for Display Inspection
Eunwoo Kim
Un Yang
Cheol Lae Roh
Stefano Ermon
DiffM
58
0
0
12 Jul 2024
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
280
30,103
0
01 Mar 2022
A Unifying Review of Deep and Shallow Anomaly Detection
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff
Jacob R. Kauffmann
Robert A. Vandermeulen
G. Montavon
Wojciech Samek
Marius Kloft
Thomas G. Dietterich
Klaus-Robert Muller
UQCV
117
800
0
24 Sep 2020
Deep Learning for Anomaly Detection: A Review
Deep Learning for Anomaly Detection: A Review
Guansong Pang
Chunhua Shen
LongBing Cao
Anton Van Den Hengel
184
925
0
06 Jul 2020
Using Self-Supervised Learning Can Improve Model Robustness and
  Uncertainty
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Dan Hendrycks
Mantas Mazeika
Saurav Kadavath
D. Song
OODSSL
56
945
0
28 Jun 2019
Combining Stochastic Adaptive Cubic Regularization with Negative
  Curvature for Nonconvex Optimization
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Seonho Park
Seung Hyun Jung
P. Pardalos
ODL
53
15
0
27 Jun 2019
Deep Semi-Supervised Anomaly Detection
Deep Semi-Supervised Anomaly Detection
Lukas Ruff
Robert A. Vandermeulen
Nico Görnitz
Alexander Binder
Emmanuel Müller
K. Müller
Marius Kloft
UQCV
58
545
0
06 Jun 2019
Exact Rate-Distortion in Autoencoders via Echo Noise
Exact Rate-Distortion in Autoencoders via Echo Noise
Rob Brekelmans
Daniel Moyer
Aram Galstyan
Greg Ver Steeg
49
17
0
15 Apr 2019
Information Theoretic Lower Bounds on Negative Log Likelihood
Information Theoretic Lower Bounds on Negative Log Likelihood
Luis A. Lastras
46
6
0
12 Apr 2019
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent
  Representations
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
Pramuditha Perera
Ramesh Nallapati
Bing Xiang
113
526
0
20 Mar 2019
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau
T. Michaeli
71
306
0
23 Jan 2019
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
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi
Ranya Almohsen
Donald Adjeroh
Gianfranco Doretto
UQCV
45
322
0
06 Jul 2018
Deep Anomaly Detection Using Geometric Transformations
Deep Anomaly Detection Using Geometric Transformations
I. Golan
Ran El-Yaniv
89
607
0
28 May 2018
Understanding disentangling in $β$-VAE
Understanding disentangling in βββ-VAE
Christopher P. Burgess
I. Higgins
Arka Pal
Loic Matthey
Nicholas Watters
Guillaume Desjardins
Alexander Lerchner
CoGeDRL
65
830
0
10 Apr 2018
Anomaly Detection using One-Class Neural Networks
Anomaly Detection using One-Class Neural Networks
Raghavendra Chalapathy
A. Menon
Sanjay Chawla
UQCV
53
395
0
18 Feb 2018
Disentangling by Factorising
Disentangling by Factorising
Hyunjik Kim
A. Mnih
CoGeOOD
62
1,350
0
16 Feb 2018
Fixing a Broken ELBO
Fixing a Broken ELBO
Alexander A. Alemi
Ben Poole
Ian S. Fischer
Joshua V. Dillon
Rif A. Saurous
Kevin Patrick Murphy
DRLBDL
61
80
0
01 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
283
8,883
0
25 Aug 2017
Sharpening Jensen's Inequality
Sharpening Jensen's Inequality
Jason Liao
Arthur Berg
45
73
0
26 Jul 2017
VAE with a VampPrior
VAE with a VampPrior
Jakub M. Tomczak
Max Welling
GANBDL
66
633
0
19 May 2017
Unsupervised Anomaly Detection with Generative Adversarial Networks to
  Guide Marker Discovery
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
T. Schlegl
Philipp Seeböck
S. Waldstein
U. Schmidt-Erfurth
Georg Langs
MedImGAN
106
2,230
0
17 Mar 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer
  Vision?
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall
Y. Gal
BDLOODUDUQCVPER
354
4,709
0
15 Mar 2017
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
158
3,454
0
07 Oct 2016
Conditional Image Generation with PixelCNN Decoders
Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord
Nal Kalchbrenner
Oriol Vinyals
L. Espeholt
Alex Graves
Koray Kavukcuoglu
VLM
209
2,513
0
16 Jun 2016
Optimization Methods for Large-Scale Machine Learning
Optimization Methods for Large-Scale Machine Learning
Léon Bottou
Frank E. Curtis
J. Nocedal
246
3,216
0
15 Jun 2016
Adversarial Autoencoders
Adversarial Autoencoders
Alireza Makhzani
Jonathon Shlens
Navdeep Jaitly
Ian Goodfellow
Brendan J. Frey
GAN
86
2,224
0
18 Nov 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
821
9,318
0
06 Jun 2015
Deep Learning and the Information Bottleneck Principle
Deep Learning and the Information Bottleneck Principle
Naftali Tishby
Noga Zaslavsky
DRL
207
1,584
0
09 Mar 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,305
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.8K
150,115
0
22 Dec 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
452
16,929
0
20 Dec 2013
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