Head First Generative Adversarial Networks From Theoretic View

126 Pages·2017·57.33 MB·English

MedGAN ID-CGAN CoGAN LR-GAN
CGAN IcGAN
AffGAN DiscoGAN
b-GAN LS-GAN AdaGAN
iGAN
LSGAN IAN
CatGAN LAPGAN
InfoGAN
AMGAN MPM-GAN
MIX+GAN
Head First Generative Adversarial Networks
McGAN
C-RNN-GAN DR-GAN
From Theoretic View
MGAN BS-GAN
GoGAN
FF-GAN
C-VAE-GAN
DCGAN
CCGAN AC-GAN
3D-GAN
Yanran Li
BiGAN
GAWWN DualGAN
CycleGAN
GP-GAN
The Hong Kong Polytechnic University
Bayesian GAN
AnoGAN
EBGAN DTN
MAGAN
Context-RNN-GAN MAD-GAN
ALI f-GAN
AL-CGAN
MARTA-GAN ArtGAN MalGAN
BEGAN
(https://github.com/kaonashi-tyc/zi2zi)
(https://github.com/junyanz/iGAN)

(Taigman et al., 2017)
(https://junyanz.github.io/CycleGAN/)
(genekogan@Twitter)
Content
Generative Adversarial Networks
•
Basics and Attractiveness
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Difficulties
•
Solution 1: Partial and Fine-grained Guidance
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Solution 2: Encoder-incorporated
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Solution 3: Wasserstein Distance
•
Content
Generative Adversarial Networks
•
Basics and Attractiveness
•
Difficulties
•
Solution 1: Partial and Fine-grained Guidance
•
Solution 2: Encoder-incorporated
•
Solution 3: Wasserstein Distance
•
Generative Adversarial Networks
(Eric Jang’s blog)