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GILBO: One Metric to Measure Them All
v1v2v3 (latest)

GILBO: One Metric to Measure Them All

13 February 2018
Alexander A. Alemi
Ian S. Fischer
    DRL
ArXiv (abs)PDFHTML

Papers citing "GILBO: One Metric to Measure Them All"

15 / 15 papers shown
Title
Information Theoretic Text-to-Image Alignment
Information Theoretic Text-to-Image Alignment
Chao Wang
Giulio Franzese
A. Finamore
Massimo Gallo
Pietro Michiardi
116
0
0
31 May 2024
Representation Learning with Contrastive Predictive Coding
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord
Yazhe Li
Oriol Vinyals
DRLSSL
320
10,302
0
10 Jul 2018
Quantitatively Evaluating GANs With Divergences Proposed for Training
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im
He Ma
Graham W. Taylor
K. Branson
EGVM
55
69
0
02 Mar 2018
MINE: Mutual Information Neural Estimation
MINE: Mutual Information Neural Estimation
Mohamed Ishmael Belghazi
A. Baratin
Sai Rajeswar
Sherjil Ozair
Yoshua Bengio
Aaron Courville
R. Devon Hjelm
DRL
194
1,279
0
12 Jan 2018
Are GANs Created Equal? A Large-Scale Study
Are GANs Created Equal? A Large-Scale Study
Mario Lucic
Karol Kurach
Marcin Michalski
Sylvain Gelly
Olivier Bousquet
EGVM
63
1,011
0
28 Nov 2017
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
Estimating Mutual Information for Discrete-Continuous Mixtures
Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao
Sreeram Kannan
Sewoong Oh
Pramod Viswanath
56
157
0
19 Sep 2017
Do GANs actually learn the distribution? An empirical study
Do GANs actually learn the distribution? An empirical study
Sanjeev Arora
Yi Zhang
49
192
0
26 Jun 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
52
53
0
15 May 2017
Precise Recovery of Latent Vectors from Generative Adversarial Networks
Precise Recovery of Latent Vectors from Generative Adversarial Networks
Zachary Chase Lipton
Subarna Tripathi
GAN
79
207
0
15 Feb 2017
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
81
223
0
14 Nov 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing
  Generative Adversarial Nets
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen
Yan Duan
Rein Houthooft
John Schulman
Ilya Sutskever
Pieter Abbeel
GAN
159
4,235
0
12 Jun 2016
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
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
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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