Generative Models

This repository is for implementation of Generative Models using Tensorflow 1.12
Description of the Papers (Korean)
The structure of the code is based on the Hwalsuk Lee's Generative Model github repository

Contributors

MMC Lab GAN Study Group members

Kyushik Min, Gihoon Kim, Hyukju Sohn, Yoonyong Ahn, Seungwon-Choi, Jongyoon Baek

Implemented Paper List (19 Papers)

GAN

Image-to-Image Translation

[CycleGAN] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
[AGGAN] Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
[StarGAN] Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
[DMIT] Multi-mapping Image-to-Image Translation via Learning Disentanglement

Interpretable GAN Latent

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

VAE

Application

Adherent Raindrop Removal with Self-Supervised Attention Maps andSpatio-Temporal Generative Adversarial Networks

Our Results

GAN Results

1. GAN

MNIST

2. DCGAN

MNIST	CelebA

3. LSGAN

MNIST	CelebA

4. WGAN

MNIST	CelebA

5. WGAN-GP

MNIST	CelebA

6. Conditional GAN

MNIST

7. InfoGAN

MNIST

8. HoloGAN

CelebA

9. SinGAN

Balloon

Mountain

Starry Night

10. PGGAN

It took about 2 weeks on TITAN RTX and trained 600k images per stage.

1024x1024 images

Cherry picked images

Latent interpolation

Fixed latent

No cherry picked images

Image-to-Image Translation Results

1. CycleGAN

Monet to Photo	Photo to Monet

Horse to Zebra	Zebra to Horse

2. AGGAN

Horse to Zebra	Zebra to Horse

3. StarGAN

CelebA

4. DMIT

Summer2Winter

Interpretable GAN Latent

1. Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

1) MNIST

VAE Results

1. VAE

Reconstruction

MNIST	CelebA

Latent Space Interpolation (MNIST)

Latent Space Interpolation (CelebA)

2. Beta-VAE

Latent Space Interpolation: Beta = 10 (CelebA)

Latent Space Interpolation: Beta = 200 (CelebA)

3. VQ-VAE

Reconstruction (MNIST)

Input	Reconstruction

Reconstruction (CelebA)

Input	Reconstruction

PixelCNN Trained Latent Decoding

MNIST	CelebA

Application Results

1. Raindrop Removal

innurman/Generative-Model

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