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Quantitatively Evaluating GANs With Divergences Proposed for Training

Quantitatively Evaluating GANs With Divergences Proposed for Training

2 March 2018
Daniel Jiwoong Im
He Ma
Graham W. Taylor
K. Branson
    EGVM
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Papers citing "Quantitatively Evaluating GANs With Divergences Proposed for Training"

31 / 31 papers shown
Title
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash
  Equilibrium
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
M. Heusel
Hubert Ramsauer
Thomas Unterthiner
Bernhard Nessler
Sepp Hochreiter
74
465
0
26 Jun 2017
The Cramer Distance as a Solution to Biased Wasserstein Gradients
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Marc G. Bellemare
Ivo Danihelka
Will Dabney
S. Mohamed
Balaji Lakshminarayanan
Stephan Hoyer
Rémi Munos
GAN
70
344
0
30 May 2017
The Numerics of GANs
The Numerics of GANs
L. Mescheder
Sebastian Nowozin
Andreas Geiger
GAN
84
456
0
30 May 2017
MMD GAN: Towards Deeper Understanding of Moment Matching Network
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li
Wei-Cheng Chang
Yu Cheng
Yiming Yang
Barnabás Póczós
GAN
66
722
0
24 May 2017
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
Ivo Danihelka
Balaji Lakshminarayanan
Benigno Uria
Daan Wierstra
Peter Dayan
GAN
50
53
0
15 May 2017
Improved Training of Wasserstein GANs
Improved Training of Wasserstein GANs
Ishaan Gulrajani
Faruk Ahmed
Martín Arjovsky
Vincent Dumoulin
Aaron Courville
GAN
189
9,545
0
31 Mar 2017
BEGAN: Boundary Equilibrium Generative Adversarial Networks
BEGAN: Boundary Equilibrium Generative Adversarial Networks
David Berthelot
Tom Schumm
Luke Metz
GAN
100
1,154
0
31 Mar 2017
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
Sanjeev Arora
Rong Ge
Yingyu Liang
Tengyu Ma
Yi Zhang
GAN
54
688
0
02 Mar 2017
Towards Principled Methods for Training Generative Adversarial Networks
Towards Principled Methods for Training Generative Adversarial Networks
Martín Arjovsky
M. Nault
GAN
79
2,105
0
17 Jan 2017
NIPS 2016 Tutorial: Generative Adversarial Networks
NIPS 2016 Tutorial: Generative Adversarial Networks
Ian Goodfellow
GAN
161
1,723
0
31 Dec 2016
Generative Adversarial Parallelization
Generative Adversarial Parallelization
Daniel Jiwoong Im
He Ma
C. Kim
Graham W. Taylor
GAN
60
38
0
13 Dec 2016
An empirical analysis of the optimization of deep network loss surfaces
An empirical analysis of the optimization of deep network loss surfaces
Daniel Jiwoong Im
Michael Tao
K. Branson
ODL
53
63
0
13 Dec 2016
Mode Regularized Generative Adversarial Networks
Mode Regularized Generative Adversarial Networks
Tong Che
Yanran Li
Athul Paul Jacob
Yoshua Bengio
Wenjie Li
GAN
101
554
0
07 Dec 2016
Generative Models and Model Criticism via Optimized Maximum Mean
  Discrepancy
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland
H. Tung
Heiko Strathmann
Soumyajit De
Aaditya Ramdas
Alex Smola
Arthur Gretton
106
260
0
14 Nov 2016
On the Quantitative Analysis of Decoder-Based Generative Models
On the Quantitative Analysis of Decoder-Based Generative Models
Yuhuai Wu
Yuri Burda
Ruslan Salakhutdinov
Roger C. Grosse
GAN
73
223
0
14 Nov 2016
Least Squares Generative Adversarial Networks
Least Squares Generative Adversarial Networks
Xudong Mao
Qing Li
Haoran Xie
Raymond Y. K. Lau
Zhen Wang
Stephen Paul Smolley
GAN
325
4,572
0
13 Nov 2016
Revisiting Classifier Two-Sample Tests
Revisiting Classifier Two-Sample Tests
David Lopez-Paz
Maxime Oquab
153
402
0
20 Oct 2016
Energy-based Generative Adversarial Network
Energy-based Generative Adversarial Network
Jiaqi Zhao
Michaël Mathieu
Yann LeCun
GAN
133
1,114
0
11 Sep 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN
3DV
733
36,781
0
25 Aug 2016
Improved Techniques for Training GANs
Improved Techniques for Training GANs
Tim Salimans
Ian Goodfellow
Wojciech Zaremba
Vicki Cheung
Alec Radford
Xi Chen
GAN
476
9,044
0
10 Jun 2016
f-GAN: Training Generative Neural Samplers using Variational Divergence
  Minimization
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin
Botond Cseke
Ryota Tomioka
GAN
141
1,655
0
02 Jun 2016
Generating images with recurrent adversarial networks
Generating images with recurrent adversarial networks
Daniel Jiwoong Im
C. Kim
Hui Jiang
Roland Memisevic
GAN
49
223
0
16 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.1K
193,814
0
10 Dec 2015
Rethinking the Inception Architecture for Computer Vision
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
Vincent Vanhoucke
Sergey Ioffe
Jonathon Shlens
Z. Wojna
3DV
BDL
855
27,350
0
02 Dec 2015
Unsupervised Representation Learning with Deep Convolutional Generative
  Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford
Luke Metz
Soumith Chintala
GAN
OOD
243
14,005
0
19 Nov 2015
Denoising Criterion for Variational Auto-Encoding Framework
Denoising Criterion for Variational Auto-Encoding Framework
Daniel Jiwoong Im
Sungjin Ahn
Roland Memisevic
Yoshua Bengio
69
195
0
19 Nov 2015
A note on the evaluation of generative models
A note on the evaluation of generative models
Lucas Theis
Aaron van den Oord
Matthias Bethge
EGVM
118
1,145
0
05 Nov 2015
DRAW: A Recurrent Neural Network For Image Generation
DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor
Ivo Danihelka
Alex Graves
Danilo Jimenez Rezende
Daan Wierstra
GAN
DRL
161
1,961
0
16 Feb 2015
NICE: Non-linear Independent Components Estimation
NICE: Non-linear Independent Components Estimation
Laurent Dinh
David M. Krueger
Yoshua Bengio
DRL
BDL
121
2,258
0
30 Oct 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.6K
100,330
0
04 Sep 2014
Neural Variational Inference and Learning in Belief Networks
Neural Variational Inference and Learning in Belief Networks
A. Mnih
Karol Gregor
BDL
169
730
0
31 Jan 2014
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