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Generative Adversarial Networks
Reading: I. Goodfellow et al., Generative adversarial networks, NIPS 2014
Adversarial Networks
Generative Model Real world Discriminative
Model
real or fake?
From https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/lectures/gan.pptx
Training Procedure: Basic Idea
• G tries to fool D
• D tries not to be fooled
• Models are trained simultaneously – As G gets better, D has a more
challenging task – As D gets better, G has a more
challenging task
• Ultimately, we don’t care about the D – Its role is to force G to work harder
Discriminative
Model
real or fake?
Generative Model
noise (Z)
Real world
From https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/lectures/gan.pptx
GAN Training Algorithm
From Goodfellow et al., 2014
http://www.iangoodfellow.com/slides/2018-06-22-gan_tutorial.pdf
http://www.iangoodfellow.com/slides/2018-06-22-gan_tutorial.pdf
Kerras et al, PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
Gradually adds layers to both generator and discriminator
Vector arithmetic
Cycle GANs (Zhu et al., 2017; arXiv:1703:10593v2 [cs.CV])
Given two image collections, algorithm learns to translate an image from one collection to the other
From https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/lectures/gan.pptx
Photos to paintings
From https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/lectures/gan.pptx
From https://www.cs.colorado.edu/~mozer/Teaching/syllabi/DeepLearningFall2017/lectures/gan.pptx
Image-to-Image Translation with Conditional Adversarial Nets Isola et al., 2017
https://phillipi.github.io/pix2pix/ https://arxiv.org/pdf/1808.06601.pdf
Image-to-Image Translation Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros
Generative Adversarial Text to Image Synthesis Reed et al.
https://github.com/reedscot/icml2016