Style gan

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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.

Technology

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

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

Stochastic variation in the generated images

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

Karras, Tero, Timo Aila, Samuli Laine, and Jaakko Lehtinen. “Progressive growing of gans
for improved quality, stability, and variation.” ICLR 2018
How？
=

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.

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

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.

펀디멘탈팀 고형권 님의 STYLE GAN2 논문 리뷰 입니다 지난번 리뷰 했던 Style gan에 이어, Style gan 2 의 논문 리뷰 입니다! Style Gan은 계속해서 Sota 자리를 유지했지만 Style Gan 내부적으로 가끔씩 물방울 모양의 artifact가 inference과정에서 큰 방해가 됨을 확인했습니다. 이와 더불어 StyleGAN에서는 AdaIN이 feature map의 평균과 분산을 normalize했지만, StyleGAN2에서는 convolution weight를 normalize한다. AdaIN에서 평균을 제거하고 표준편차만 사용하였고, 표준편차만으로도 충분하다는 것을 알게 되었다. 또한. bias와 noise를 block 외부로 빼서 style과 noise의 영향력을 독립시켰습니다. 기존에는 noise의 영향력이 style의 크기에 반비례하였으나, noise의 변화에 따른 효과가 분명해졌습니다. 이는 Instance Normalization과 수학적으로 동일한 방법은 아니지만, output feature map을 standard unit standard deviation을 갖도록 해주어 학습을 더욱 안정적으로 만들며 물방울 artifact를 없애는데도 큰 성과를 이루어 냈습니다! 오늘도 많은 관심 미리 감사드립니다!

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Style gan

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？ =

10.

11.

12.

13.

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

19.

20. Perceptual path length (Smooth)

21.

22. Linear separability on CelebA-HQ 1 2 3

23.

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.

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