StyleGAN: Difference between revisions
Created page with "StyleGAN CVPR 2019<br> [https://arxiv.org/abs/1812.04948 2018 Paper (arxiv)] [http://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_f..." |
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==Architecture== | ==Architecture== | ||
[[File:StyleGAN architecture.PNG | thumb | 300px | Architecture of StyleGAN from their paper]] | [[File:StyleGAN architecture.PNG | thumb | 300px | Architecture of StyleGAN from their paper]] | ||
StyleGAN consists of a mapping network <math>f</math> and a synthesis network <math>g</math>. | |||
===Mapping Network=== | |||
The mapping network <math>f</math> consists of 8 fully connected layers with leaky relu activations at each layer. | |||
===Synthesis Network=== | |||
The synthesis network is based on progressive growing (ProGAN). | |||
It consists of 9 convolution blocks, one for each resolution from <math>4^2</math> to <math>1024^2</math>.<br> | |||
Each block consists of upsample, 3x3 convolution, AdaIN, 3x3 convolution, AdaIN. | |||
After each convolution layer, a gaussian noise with learned variance is added to the feature maps. | |||
====Adaptive Instance Normalization==== | |||
==Results== | ==Results== | ||
Revision as of 18:59, 4 March 2020
StyleGAN CVPR 2019
2018 Paper (arxiv)
CVPR 2019 Open Access
StyleGAN Github
StyleGAN2 Paper
StyleGAN2 Github
An architecture by Nvidia which allows controlling the "style" of the GAN output by applying adaptive instance normalization at different layers of the network.
StyleGAN2 improves upon this by...
Architecture
StyleGAN consists of a mapping network \(\displaystyle f\) and a synthesis network \(\displaystyle g\).
Mapping Network
The mapping network \(\displaystyle f\) consists of 8 fully connected layers with leaky relu activations at each layer.
Synthesis Network
The synthesis network is based on progressive growing (ProGAN).
It consists of 9 convolution blocks, one for each resolution from \(\displaystyle 4^2\) to \(\displaystyle 1024^2\).
Each block consists of upsample, 3x3 convolution, AdaIN, 3x3 convolution, AdaIN.
After each convolution layer, a gaussian noise with learned variance is added to the feature maps.