This document summarizes a paper on Style GAN, which proposes a style-based GAN that can control image generation at multiple levels of style. It introduces new evaluation methods and collects a larger, more varied dataset (FFHQ). The paper aims to disentangle style embeddings to allow unsupervised separation of high-level attributes and introduce stochastic variation in generated images through control of the network architecture.

1. Style GAN
Zhedong Zheng
University of Technology Sydney
2019-1-12

2. What？
• Multiple levels of style
• Propose a style-based GAN
• New Evaluation Methods
• Collect a larger and various dataset FFHQ

3. Unsupervised separation of high-level attributes
Middle style
Coarse style
Fine style

4. Stochastic variation in the generated images

5. It demands …
• Network architecture to control style
• Style embedding Disentanglement (Separation)

6. What？
• Multiple levels of style
• Propose a style-based GAN
• New Evaluation Methods
• Collect a larger and various dataset FFHQ

7. Karras, Tero, Timo Aila, Samuli Laine, and Jaakko Lehtinen. “Progressive growing of gans
for improved quality, stability, and variation.” ICLR 2018
How？
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14. What？
• Multiple levels of style
• Propose a style-based GAN
• New Evaluation Methods
• Collect a larger and various dataset FFHQ

15. FID on 2 Datasets
BigGAN:

16. Mix regularization
To be specific, we run two latent codes z1, z2
through the mapping network, and have the
corresponding w1, w2 control the styles so that w1
applies before the crossover point and w2 after it.

17. Mix regularization

18. If feature is entangled…
• FID evaluates the image quality.
• Bad Interpolation (multiple variants)
• Bad Classification

20. Perceptual path length (Smooth)

22. Linear separability on CelebA-HQ
1
2
3

24. What？
• Multiple levels of style
• Propose a style-based GAN
• New Evaluation Methods
• Collect a larger and various dataset FFHQ

25. Reference
• Karras, Tero, Samuli Laine, and Timo Aila. "A Style-Based Generator
Architecture for Generative Adversarial Networks." arXiv preprint
arXiv:1812.04948 (2018).
• Karras, Tero, Timo Aila, Samuli Laine, and Jaakko Lehtinen. “Progressive
growing of gans for improved quality, stability, and variation.” ICLR
2018
• Brock, Andrew, Jeff Donahue, and Karen Simonyan. "Large scale gan
training for high fidelity natural image synthesis." ICLR 2018.