This document discusses StyleGAN, a style-based generator architecture for generative adversarial networks (GANs). StyleGAN uses adaptive instance normalization (AdaIN) to separate high-level attributes like content from style by modulating the instance statistics of intermediate features. It achieves state-of-the-art results on face generation. The document outlines the StyleGAN architecture, compares it to a BigGAN-like model, and discusses experiments applying StyleGAN2 to face datasets and BigGAN to other domains.
4. Style GAN A Style-Based Generator Architecture for Generative Adversarial Networks
5. Style GAN
Z W
A Style-Based Generator Architecture for Generative Adversarial Networks
6. Style GAN A Style-Based Generator Architecture for Generative Adversarial Networks
z w
z ∈ Z w ∈ W
f : Z → W
f W
7. Style GAN
AdaIN (xi, y) = ys,i
xi − μ (xi)
σ (xi)
+ yb,i
w → y = (yb, ys)
yb
ys
A Style-Based Generator Architecture for Generative Adversarial Networks
w
w
8. Style GAN A Style-Based Generator Architecture for Generative Adversarial Networks
9. Method StyleGAN-like layer-wise model
z ∼ p(z)
I = G(z)
G1, G2, ⋯, GL ∈ G
Gi
I = GL (yL−1)
yi = Gi (yi−1, w) w = M(z)
w′ = w + Vx
I′ = G (y0, w′)
w
V :
x (xk = 0) :
x
10. Method BigGAN-like model more complex …
yi = Gi (yi−1, z)
z′ = z + Ux
I′ = G(z′)
U :
x (xk = 0) :
z
xj = VT
(yj − μ) V :
