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Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz
  Discriminators

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

26 May 2019
Alexander Tong
Guy Wolf
Smita Krishnaswamy
ArXivPDFHTML

Papers citing "Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators"

8 / 8 papers shown
Title
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
108
526
0
20 Mar 2019
Deep Learning for Anomaly Detection: A Survey
Deep Learning for Anomaly Detection: A Survey
Raghavendra Chalapathy
Sanjay Chawla
AI4TS
154
1,494
0
10 Jan 2019
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi
Ranya Almohsen
Donald Adjeroh
Gianfranco Doretto
UQCV
40
321
0
06 Jul 2018
Latent Space Autoregression for Novelty Detection
Latent Space Autoregression for Novelty Detection
Davide Abati
Angelo Porrello
Simone Calderara
Rita Cucchiara
100
434
0
04 Jul 2018
Improved Training of Wasserstein GANs
Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martín Arjovsky
Vincent Dumoulin
Aaron Courville
GAN
193
9,545
0
31 Mar 2017
Improved Techniques for Training GANs
Improved Techniques for Training GANs
Tim Salimans
Ian Goodfellow
Wojciech Zaremba
Vicki Cheung
Alec Radford
Xi Chen
GAN
478
9,044
0
10 Jun 2016
Asynchrony begets Momentum, with an Application to Deep Learning
Asynchrony begets Momentum, with an Application to Deep Learning
Jeff Donahue
Philipp Krahenbuhl
Stefan Hadjis
Christopher Ré
92
141
0
31 May 2016
Adversarial Autoencoders
Adversarial Autoencoders
Alireza Makhzani
Jonathon Shlens
Navdeep Jaitly
Ian Goodfellow
Brendan J. Frey
GAN
86
2,223
0
18 Nov 2015
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