Introduction to GAN

Introduction to GAN
서울대학교 방사선의학물리연구실
이 지 민 ( ljm861@gmail.com )

참고 자료 출처 (본 슬라이드 인용 순)
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Contents
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1. Generative Model
2. Auto-Regressive Models
3. Variational Auto-Encoder
4. Generative Adversarial Networks
5. Significant Variants & Applications

Generative Model
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Ideal Generative Model
1.
CAT
Short
Hair
Big
Ear
Model

Generative Model
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1.
CAT
Short
Hair
Big
Ear
Model

Generative Model
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1.
CAT
Short
Hair
Big
Ear
Tabby
Model

Generative Model
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1.
CAT
Short
Hair
Big
Ear
Tabby
Savannah Cat
Model

Generative Model
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1.
https://www.slideshare.net/carpedm20/pycon-korea-2016 (지적 대화를 위한 깊고 넓은 딥러닝, 김태훈)

Generative Model
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1.

Generative Model
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Generative Model
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Generative Model
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1.

Generative Model
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1.
https://blog.openai.com/generative-models/

Generative Model
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Why Generative Model
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Li, Yijun, et al., Generative face completion, 2017

Generative Model
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Deep Generative Models
1.
 Auto-Regressive Models
 Variational Auto-Encoder
 Generative Adversarial Networks

Auto-Regressive Models
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Pixel-by-pixel generation
2.
http://slazebni.cs.illinois.edu/spring17/lec13_advanced.pdf

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Multi-Dimensional RNNs (2013)
2.
Graves et al, Multi-Dimensional Recurrent Neural Networks, 2013

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Spatial LSTM (2015)
2.
Theis et al., Generative Image Modeling Using Spatial LSTMs, 2015

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Pixel RNN (2016)
2.
Aaron et al, Pixel Recurrent Neural Networks, 2016

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Sampling
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Sampling
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Sampling
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Sampling
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Sampling
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Pixel RNN (2016)
2.
Aaron et al, Pixel Recurrent Neural Networks, 2016

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Features
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Variational Auto-Encoder
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Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/

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Auto-Encoder
3.
http://kvfrans.com/variational-autoencoders-explained/
?

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3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf

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3.
http://kvfrans.com/variational-autoencoders-explained/, https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)

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3.

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3.

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Kullback-Leibler Divergence
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https://en.wikipedia.org/wiki/Kullback–Leibler_divergence

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Loss function
3.
https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/08_Autoencoder/%5B2%EA%B8%B0%5DAutoEncoder.pdf (최건호)

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Reparameterization Trick
3.
Carl Doersch, Tutorial on Variational Autoencoders, 2016

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Results
3.
Kingma et al., Auto-Encoding Variational Bayes, 2014

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Results
3.
https://www.slideshare.net/HyungjooCho2/deep-generative-modelpdf (Deep Generative Models, 조형주)

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Features
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Generative Adversarial Networks
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4.

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4.
생성 모델

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4.
적대적 학습

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4.
적대적 학습
Generator vs Discriminator

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4.
https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f

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4.
https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091 (Generative Adversarial Networks, 김남주)

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Value Function
4.

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Value Function
4.
https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network (1시간 만에 GAN 완전 정복하기, 최윤제)

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Value Function
4.
https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network (1시간 만에 GAN 완전 정복하기, 최윤제)

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4.
Goodfellow et al, Generative Adversarial Networks, 2014

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Results
4.
Goodfellow et al, Generative Adversarial Networks, 2014

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Features
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Comparison with Auto-regressive models and VAE
4.
http://slazebni.cs.illinois.edu/spring17/lec13_advanced.pdf, https://openai.com/blog/generative-models/

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DCGAN (Deep Convolutional GAN)
4.
Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015

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DCGAN (Deep Convolutional GAN)
4.
Radford et al, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015 / https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6

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Pytorch Implementation
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https://github.com/GunhoChoi/PyTorch-FastCampus/blob/master/09_GAN/1_DCGAN/DCGAN.ipynb (최건호)

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Issues during Training
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Mode collapsing / Oscillating
Metz et al, Unrolled Generative Adversarial Networks, 2016
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Target MLE (=KL) JS Reverse KL

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4.
Metz et al, Unrolled Generative Adversarial Networks, 2016

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Intractable loss
4.
https://www.slideshare.net/ssuser7e10e4/wasserstein-gan-i (Wasserstein GAN 수학 이해하기, 임성빈)

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Intractable loss
4.

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Intractable loss
4.

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Intractable loss
4.
Vanilla GAN
LSGAN WGAN

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Intractable loss
4.
Arjovsky et al, Wasserstein Generative Adversarial Networks, 2017

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Intractable loss
4.
Arjovsky et al, Wasserstein Generative Adversarial Networks, 2017

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Balance between Generator & Discriminator
4.
Berthelot et al, BEGAN, 2017

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Manipulation
4.
Mirza et al, Conditional Generative Adversarial Networks, 2014

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Quality
4.
Karras et al, Progressive Growing of GANs For Improved Quality, Stability, and Variation, 2017

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Quality
4.
https://www.youtube.com/watch?v=XOxxPcy5Gr4

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Quality
4.
Karras et al, Progressive Growing of GANs For Improved Quality, Stability, and Variation, 2017

Significant Variants & Applications
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Significant Variants
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Info GAN
5.
Chen et al, InfoGAN, 2017

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Pix2Pix
5.
Isola et al, Image-to-image translation with conditional GAN, 2016

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Domain Cross GAN
5.
Taigman et al, Unsupervised Cross-Domain Image Generation, 2016

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CycleGAN
5.
Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, 2017

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CycleGAN
5.
Zhu et al, Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, 2017

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DiscoGAN
5.
Kim et al, Learning to Discover Cross Domain Relations with Generative Adversarial Networks, 2017

Applications
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Time Series Generation (InfoGAN)
5.
https://github.com/buriburisuri/timeseries_gan (김남주)

Applications
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ehrGAN
5.
Che et al., Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records, 2017

Applications
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RCGAN
5.
Che et al., Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records, 2017

Applications
109
GAN for Low-dose CT
5.
Jelmer M. et al, Generative Adversarial Networks for Noise Reduction in Low-Dose CT, 2017, http://medicine.utah.edu/radiology/news/2016/low-dose-ct-zeng-award.php

Applications
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GAN for Low-dose CT
5.
Jelmer M. et al, Generative Adversarial Networks for Noise Reduction in Low-Dose CT, 2017

Applications
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Stain Style Transfer
5.
H Cho et al, Neural Stain-Style Transfer Learning using GAN for Histopathological Images, 2017

Applications
112
Simulated & Unsupervised Learning
5.
Shrivastava et al, Learning from Simulated and Unsupervised Images through Adversarial Training, 2016

Applications
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AnoGAN
5.
Schlegl et al, Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker discovery, 2017

Introduction to GAN

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