StyleGAN: Difference between revisions

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===Mapping Network===

The mapping network <math>f</math> consists of 8 fully connected layers with leaky relu activations at each layer.

The goal of the mapping network is to generate a latent vector <math>w</math>.

This latent <math>w</math> is used by the synthesis network as input the each AdaIN block.

Before each AdaIN block, a learned affine transformation converts <math>w</math> into a "style" in the form of mean and standard deviation.

The mapping network <math>f</math> consists of 8 fully connected layers with leaky relu activations at each layer.

The input and output of this vector is an array of size 512.

===Synthesis Network===

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It consists of 9 convolution blocks, one for each resolution from <math>4^2</math> to <math>1024^2</math>.

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.

After each convolution layer, a gaussian noise with learned variance (block B in the figure) is added to the feature maps.

====Adaptive Instance Normalization====

Each AdaIN block takes as input the latent style <math>w</math> and the feature map <math>x</math>.

An affine layer (fully connected with no activation, block A in the figure) converts the style to a mean <math>y_{b,i}</math> and standard deviation <math>y_{s,i}</math>.

Then the feature map is shifted and scaled to have this mean and standard deviation.

* <math>\operatorname{AdaIN}(\mathbf{x}_i, \mathbf{y}) = \mathbf{y}_{s,i}\frac{\mathbf{x}_i - \mu(\mathbf{x}_i)}{\sigma(\mathbf{x}_i)} + \mathbf{y}_{b,i}</math>

==Results==

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==Related==

* [https://arxiv.org/abs/1904.03189 Image2StyleGAN]

==Resources==

* [https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/ https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/]

* [https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431 https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431]

@@ Line 12: / Line 12: @@
 ===Mapping Network===
-The mapping network <math>f</math> consists of 8 fully connected layers with leaky relu activations at each layer.
+The goal of the mapping network is to generate a latent vector <math>w</math>.<br>
+This latent <math>w</math> is used by the synthesis network as input the each AdaIN block.<br>
+Before each AdaIN block, a learned affine transformation converts <math>w</math> into a "style" in the form of mean and standard deviation.<br>
+The mapping network <math>f</math> consists of 8 fully connected layers with leaky relu activations at each layer.<br>
+The input and output of this vector is an array of size 512.<br>
 ===Synthesis Network===
@@ Line 18: / Line 22: @@
 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.
+After each convolution layer, a gaussian noise with learned variance (block B in the figure) is added to the feature maps.
 ====Adaptive Instance Normalization====
+Each AdaIN block takes as input the latent style <math>w</math> and the feature map <math>x</math>.<br>
+An affine layer (fully connected with no activation, block A in the figure) converts the style to a mean <math>y_{b,i}</math> and standard deviation <math>y_{s,i}</math>.<br>
+Then the feature map is shifted and scaled to have this mean and standard deviation.<br>
+* <math>\operatorname{AdaIN}(\mathbf{x}_i, \mathbf{y}) = \mathbf{y}_{s,i}\frac{\mathbf{x}_i - \mu(\mathbf{x}_i)}{\sigma(\mathbf{x}_i)} + \mathbf{y}_{b,i}</math>
 ==Results==
@@ Line 29: / Line 37: @@
 ==Related==
 * [https://arxiv.org/abs/1904.03189 Image2StyleGAN]
+==Resources==
+* [https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/ https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/]
+* [https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431 https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431]