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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"

15 / 15 papers shown
Title
Divergence Frontiers for Generative Models: Sample Complexity,
  Quantization Effects, and Frontier Integrals
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Lang Liu
Krishna Pillutla
Sean Welleck
Sewoong Oh
Yejin Choi
Zaïd Harchaoui
MQ
25
14
0
15 Jun 2021
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating
  and Auditing Generative Models
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
Ahmed Alaa
B. V. Breugel
Evgeny S. Saveliev
M. Schaar
47
186
0
17 Feb 2021
On the Evaluation of Generative Adversarial Networks By Discriminative
  Models
On the Evaluation of Generative Adversarial Networks By Discriminative Models
A. Torfi
Mohammadreza Beyki
Edward A. Fox
EGVM
21
7
0
07 Oct 2020
Conditional GAN for timeseries generation
Conditional GAN for timeseries generation
Kaleb E. Smith
Anthony O. Smith
AI4TS
6
77
0
30 Jun 2020
A Review on Generative Adversarial Networks: Algorithms, Theory, and
  Applications
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui
Zhenan Sun
Yonggang Wen
Dacheng Tao
Jieping Ye
EGVM
26
817
0
20 Jan 2020
Towards GAN Benchmarks Which Require Generalization
Towards GAN Benchmarks Which Require Generalization
Ishaan Gulrajani
Colin Raffel
Luke Metz
16
57
0
10 Jan 2020
Synthesis of Realistic ECG using Generative Adversarial Networks
Synthesis of Realistic ECG using Generative Adversarial Networks
Anne Marie Delaney
Eoin Brophy
T. Ward
24
79
0
19 Sep 2019
DeepScaffold: a comprehensive tool for scaffold-based de novo drug
  discovery using deep learning
DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
Yibo Li
Jianxing Hu
Yanxing Wang
Jielong Zhou
L. Zhang
Zhenming Liu
22
92
0
20 Aug 2019
Bias Correction of Learned Generative Models using Likelihood-Free
  Importance Weighting
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover
Jiaming Song
Alekh Agarwal
Kenneth Tran
Ashish Kapoor
Eric Horvitz
Stefano Ermon
26
123
0
23 Jun 2019
Beyond Local Nash Equilibria for Adversarial Networks
Beyond Local Nash Equilibria for Adversarial Networks
F. Oliehoek
Rahul Savani
Jose Gallego-Posada
Elise van der Pol
R. Groß
GAN
10
43
0
18 Jun 2018
Assessing Generative Models via Precision and Recall
Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi
Olivier Bachem
Mario Lucic
Olivier Bousquet
Sylvain Gelly
EGVM
13
565
0
31 May 2018
On GANs and GMMs
On GANs and GMMs
Eitan Richardson
Yair Weiss
GAN
17
149
0
31 May 2018
Transferring GANs: generating images from limited data
Transferring GANs: generating images from limited data
Yaxing Wang
Chenshen Wu
Luis Herranz
Joost van de Weijer
Abel Gonzalez-Garcia
Bogdan Raducanu
13
283
0
04 May 2018
Multi-Objective De Novo Drug Design with Conditional Graph Generative
  Model
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Yibo Li
L. Zhang
Zhenming Liu
29
335
0
18 Jan 2018
Revisiting Classifier Two-Sample Tests
Revisiting Classifier Two-Sample Tests
David Lopez-Paz
Maxime Oquab
78
390
0
20 Oct 2016
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