SlideShare a Scribd company logo

Style gan2 review

•
•
T
taeseon ryu

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!

Science
1 of 42
Download to read offline
Analyzing and Improving the Image Quality of StyleGAN 펀디멘탈 팀: 송헌, 고형권, 김동희, 김창연, 이민경, 이재윤 발표: 고형권 (hyungkwonko@gmail.com)
Progressive GAN (2018 ICLR) recap Image from original paper 2
Image from original paper StyleGAN (2019 CVPR) recap 3
Image from original paper StyleGAN (2019 CVPR) recap 4
Image from original paper StyleGAN (2019 CVPR) recap 5
https://github.com/justinpinkney/awesome-pretrained-stylegan2 Pretrained models for different datasets 6
Q & A
StyleGAN2 (2020 CVPR) ● In this paper, we focus all analysis solely on W, as it is the relevant latent space from the synthesis network’s point of view. 8
StyleGAN2 (2020 CVPR) Image from original paper 9
Image from original paper The Problem of StyleGAN (Droplet artifacts) 10
The Problem of StyleGAN (Droplet artifacts) https://www.youtube.com/watch?v=c-NJtV9Jvp0 11
B. Weight Demodulation ● AdaIN operation normalizes the mean and variance of each feature map separately, thereby potentially destroying any information found in the magnitudes of the features relative to each other. ● How to fix? Get rid of AdaIN structure 12
B. Weight Demodulation ● BEFORE (StyleGAN) ● W → affine transform → A Image from original paper 13
B. Weight Demodulation ● AFTER (StyleGAN2) Image from original paper 14
B. Weight Demodulation Image from original paper 15
The Result of Weight Demodulation https://www.youtube.com/watch?v=c-NJtV9Jvp0 16
The Result of Weight Demodulation (in style mixing) https://www.youtube.com/watch?v=c-NJtV9Jvp0 17
Q & A
Perceptual Path Length score ● Perceptual Path Length score (PPL): Sampled from latent-space vectors using interpolation → distance calculation using features of VGG Image from original paper 19
Perceptual Path Length score Image from original paper 20
Why LOW PPL == GOOD Quality? ● NOT immediately obvious… ● Hypothesis: GOOD image region (STRETCHED, large latent space) BAD image region (SQUEEZED, small latent space) Image from original paper 21
Perceptual Path Length score 22
Why better than FID and Precision & Recall? Geirhos et al., 2019 ICLR (oral) 23
D. Path Length Regularization ● We would like to encourage that a fixed-size step in W results in a non-zero, fixed-magnitude change in the image. ● We can measure the deviation from this ideal empirically by stepping into random directions in the image space and observing the corresponding w gradients. Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow. Is generator conditioning causally related to GAN performance? CoRR, abs/1802.08768, 2018. 24
C. Lazy Regularization ● As the resulting regularization term is somewhat expensive to compute, we first describe a general optimization that applies to any regularization technique. ● … performed only once every 16 mini-batches 25
Q & A
The Problem of StyleGAN (Phase artifacts) Image from original paper 27
The Problem of StyleGAN (Phase artifacts) https://www.youtube.com/watch?v=c-NJtV9Jvp0 28
The Problem of StyleGAN (Phase artifacts) ● … We believe the problem is that in progressive growing each resolution serves momentarily as the output resolution, forcing it to generate maximal frequency details, which then leads to the trained network to have excessively high frequencies in the intermediate layers, ... 29
MSG-GAN (2020 CVPR) Image from original paper 30
E. No Growing, New G & D Architecture Image from original paper 31
E. No Growing, New G & D Architecture Image from original paper 32
The Result of New Architecture https://www.youtube.com/watch?v=c-NJtV9Jvp0 33
F. Large Networks for Better Performance … To verify this, we inspected the generated images manually and noticed that they generally lack some of the pixel-level detail... Image from original paper 34
F. Large Networks for Better Performance Image from original paper 35
Deep Fake Detection Algorithm ● How can we detect generated / synthesized image? Image from original paper 36
Deep Fake Detection Algorithm https://www.youtube.com/watch?v=c-NJtV9Jvp0 37
How to do this? LPIPS distance Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 586-595). 38
How to do this? ● (for StyleGAN) it appears that the mapping from W to images is too complex for this to succeed reliably in practice. ● We find it encouraging that StyleGAN2 makes source attribution easier even though the image quality has improved significantly. Image from original paper 39
https://github.com/justinpinkney/a wesome-pretrained-stylegan2 Many pre-trained models exist 40
Outro 41
Q & A

Recommended

Style space analysis paper review !
Style space analysis paper review !Style space analysis paper review !
Style space analysis paper review !taeseon ryu
 
A Style-Based Generator Architecture for Generative Adversarial Networks
A Style-Based Generator Architecture for Generative Adversarial NetworksA Style-Based Generator Architecture for Generative Adversarial Networks
A Style-Based Generator Architecture for Generative Adversarial Networksivaderivader
 
Style gan
Style ganStyle gan
Style gan哲东 郑
 
Evolution of the StyleGAN family
Evolution of the StyleGAN familyEvolution of the StyleGAN family
Evolution of the StyleGAN familyVitaly Bondar
 
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIGenerative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
 
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs) A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
 
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Amol Patil
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 

Recommended

Style space analysis paper review !
Style space analysis paper review !Style space analysis paper review !
Style space analysis paper review !taeseon ryu
 
A Style-Based Generator Architecture for Generative Adversarial Networks
A Style-Based Generator Architecture for Generative Adversarial NetworksA Style-Based Generator Architecture for Generative Adversarial Networks
A Style-Based Generator Architecture for Generative Adversarial Networksivaderivader
 
Style gan
Style ganStyle gan
Style gan哲东 郑
 
Evolution of the StyleGAN family
Evolution of the StyleGAN familyEvolution of the StyleGAN family
Evolution of the StyleGAN familyVitaly Bondar
 
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIGenerative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
 
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs) A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
 
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Amol Patil
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsArtifacia
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
GAN Evaluation
GAN EvaluationGAN Evaluation
GAN EvaluationDongheon Lee
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative ModelsMLReview
 
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image SynthesisBigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image SynthesisYoung Seok Kim
 
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Vitaly Bondar
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
A friendly introduction to GANs
A friendly introduction to GANsA friendly introduction to GANs
A friendly introduction to GANsCsongor Barabasi
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Manohar Mukku
 
Generative adversarial text to image synthesis
Generative adversarial text to image synthesisGenerative adversarial text to image synthesis
Generative adversarial text to image synthesisUniversitat Politècnica de Catalunya
 
Score based generative model
Score based generative modelScore based generative model
Score based generative modelsangyun lee
 
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAIYurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAILviv Startup Club
 
Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)DaeJin Kim
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksBennoG1
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료종현 최
 
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN ImageryDeep Learning JP
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGANNAVER Engineering
 
A brief introduction to recent segmentation methods
A brief introduction to recent segmentation methodsA brief introduction to recent segmentation methods
A brief introduction to recent segmentation methodsShunta Saito
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018
Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018
Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018Universitat Politècnica de Catalunya
 
Advances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksAdvances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksFörderverein Technische Fakultät
 

More Related Content

What's hot

Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsArtifacia
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative ModelsMLReview
 
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image SynthesisBigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image SynthesisYoung Seok Kim
 
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Vitaly Bondar
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
A friendly introduction to GANs
A friendly introduction to GANsA friendly introduction to GANs
A friendly introduction to GANsCsongor Barabasi
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Manohar Mukku
 
Score based generative model
Score based generative modelScore based generative model
Score based generative modelsangyun lee
 
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAIYurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAILviv Startup Club
 
Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)DaeJin Kim
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksBennoG1
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료종현 최
 
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN ImageryDeep Learning JP
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGANNAVER Engineering
 
A brief introduction to recent segmentation methods
A brief introduction to recent segmentation methodsA brief introduction to recent segmentation methods
A brief introduction to recent segmentation methodsShunta Saito
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 

What's hot (20)

Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their Applications
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
GAN Evaluation
GAN EvaluationGAN Evaluation
GAN Evaluation
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative Models
 
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image SynthesisBigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
 
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
Imagen: Photorealistic Text-to-Image Diffusion Models with Deep Language Unde...
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and Applications
 
A friendly introduction to GANs
A friendly introduction to GANsA friendly introduction to GANs
A friendly introduction to GANs
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)
 
Generative adversarial text to image synthesis
Generative adversarial text to image synthesisGenerative adversarial text to image synthesis
Generative adversarial text to image synthesis
 
Score based generative model
Score based generative modelScore based generative model
Score based generative model
 
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAIYurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
Yurii Pashchenko: Zero-shot learning capabilities of CLIP model from OpenAI
 
Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)Research Trends in Editing image using GAN (TAGAN, Editable GAN)
Research Trends in Editing image using GAN (TAGAN, Editable GAN)
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial Networks
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료
 
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
【DL輪読会】StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGAN
 
A brief introduction to recent segmentation methods
A brief introduction to recent segmentation methodsA brief introduction to recent segmentation methods
A brief introduction to recent segmentation methods
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 

Similar to Style gan2 review

Advances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksAdvances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksFörderverein Technische Fakultät
 
Automatic Building detection for satellite Images using IGV and DSM
Automatic Building detection for satellite Images using IGV and DSMAutomatic Building detection for satellite Images using IGV and DSM
Automatic Building detection for satellite Images using IGV and DSMAmit Raikar
 
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...Universitat Politècnica de Catalunya
 
Not Enough Measurements, Too Many Measurements
Not Enough Measurements, Too Many MeasurementsNot Enough Measurements, Too Many Measurements
Not Enough Measurements, Too Many MeasurementsMike McCann
 
Convolutional Neural Networks CNN
Convolutional Neural Networks CNNConvolutional Neural Networks CNN
Convolutional Neural Networks CNNAbdullah al Mamun
 
Contrast and resolution improvement of pocus using self consistent cycle gan
Contrast and resolution improvement of pocus using self consistent cycle ganContrast and resolution improvement of pocus using self consistent cycle gan
Contrast and resolution improvement of pocus using self consistent cycle ganShujaat Khan
 
Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksTutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview
 
Evaluation of conditional images synthesis: generating a photorealistic image...
Evaluation of conditional images synthesis: generating a photorealistic image...Evaluation of conditional images synthesis: generating a photorealistic image...
Evaluation of conditional images synthesis: generating a photorealistic image...SamanthaGallone
 
Unpaired Image Translations Using GANs: A Review
Unpaired Image Translations Using GANs: A ReviewUnpaired Image Translations Using GANs: A Review
Unpaired Image Translations Using GANs: A ReviewIRJET Journal
 
Decomposing image generation into layout priction and conditional synthesis
Decomposing image generation into layout priction and conditional synthesisDecomposing image generation into layout priction and conditional synthesis
Decomposing image generation into layout priction and conditional synthesisNaeem Shehzad
 
IRJET - mage Colorization using Self Attention GAN
IRJET - mage Colorization using Self Attention GANIRJET - mage Colorization using Self Attention GAN
IRJET - mage Colorization using Self Attention GANIRJET Journal
 
Image Translation with GAN
Image Translation with GANImage Translation with GAN
Image Translation with GANJunho Cho
 
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...MYEONGGYU LEE
 
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET Transform
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET TransformEnhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET Transform
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET TransformMonika Rout
 
Transformer in Vision
Transformer in VisionTransformer in Vision
Transformer in VisionSangmin Woo
 
Project_Final_Review.pdf
Project_Final_Review.pdfProject_Final_Review.pdf
Project_Final_Review.pdfDivyaGugulothu
 
B-DCGAN Slides for ICONIP2019
B-DCGAN Slides for ICONIP2019B-DCGAN Slides for ICONIP2019
B-DCGAN Slides for ICONIP2019Hideo Terada
 
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...Proposal and Implementation of the Connected-Component Labeling of Binary Ima...
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...CSCJournals
 

Similar to Style gan2 review (20)

Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018
Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018
Deep Generative Models - Kevin McGuinness - UPC Barcelona 2018
 
Advances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial NetworksAdvances in Visual Quality Restoration with Generative Adversarial Networks
Advances in Visual Quality Restoration with Generative Adversarial Networks
 
Automatic Building detection for satellite Images using IGV and DSM
Automatic Building detection for satellite Images using IGV and DSMAutomatic Building detection for satellite Images using IGV and DSM
Automatic Building detection for satellite Images using IGV and DSM
 
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...
Generative Models and Adversarial Training (D2L3 Insight@DCU Machine Learning...
 
Not Enough Measurements, Too Many Measurements
Not Enough Measurements, Too Many MeasurementsNot Enough Measurements, Too Many Measurements
Not Enough Measurements, Too Many Measurements
 
Convolutional Neural Networks CNN
Convolutional Neural Networks CNNConvolutional Neural Networks CNN
Convolutional Neural Networks CNN
 
Contrast and resolution improvement of pocus using self consistent cycle gan
Contrast and resolution improvement of pocus using self consistent cycle ganContrast and resolution improvement of pocus using self consistent cycle gan
Contrast and resolution improvement of pocus using self consistent cycle gan
 
Tutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial NetworksTutorial on Theory and Application of Generative Adversarial Networks
Tutorial on Theory and Application of Generative Adversarial Networks
 
Evaluation of conditional images synthesis: generating a photorealistic image...
Evaluation of conditional images synthesis: generating a photorealistic image...Evaluation of conditional images synthesis: generating a photorealistic image...
Evaluation of conditional images synthesis: generating a photorealistic image...
 
Unpaired Image Translations Using GANs: A Review
Unpaired Image Translations Using GANs: A ReviewUnpaired Image Translations Using GANs: A Review
Unpaired Image Translations Using GANs: A Review
 
Decomposing image generation into layout priction and conditional synthesis
Decomposing image generation into layout priction and conditional synthesisDecomposing image generation into layout priction and conditional synthesis
Decomposing image generation into layout priction and conditional synthesis
 
IRJET - mage Colorization using Self Attention GAN
IRJET - mage Colorization using Self Attention GANIRJET - mage Colorization using Self Attention GAN
IRJET - mage Colorization using Self Attention GAN
 
Image Translation with GAN
Image Translation with GANImage Translation with GAN
Image Translation with GAN
 
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...
(Paper Review)U-GAT-IT: unsupervised generative attentional networks with ada...
 
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET Transform
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET TransformEnhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET Transform
Enhancement Of Retinal Fundus Image Using 2-D GABOR WAVELET Transform
 
Transformer in Vision
Transformer in VisionTransformer in Vision
Transformer in Vision
 
MAHA DISS PROJECT 1-1.pptx
MAHA DISS PROJECT 1-1.pptxMAHA DISS PROJECT 1-1.pptx
MAHA DISS PROJECT 1-1.pptx
 
Project_Final_Review.pdf
Project_Final_Review.pdfProject_Final_Review.pdf
Project_Final_Review.pdf
 
B-DCGAN Slides for ICONIP2019
B-DCGAN Slides for ICONIP2019B-DCGAN Slides for ICONIP2019
B-DCGAN Slides for ICONIP2019
 
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...Proposal and Implementation of the Connected-Component Labeling of Binary Ima...
Proposal and Implementation of the Connected-Component Labeling of Binary Ima...
 

More from taeseon ryu

OpineSum Entailment-based self-training for abstractive opinion summarization...
OpineSum Entailment-based self-training for abstractive opinion summarization...OpineSum Entailment-based self-training for abstractive opinion summarization...
OpineSum Entailment-based self-training for abstractive opinion summarization...taeseon ryu
 
3D Gaussian Splatting
3D Gaussian Splatting3D Gaussian Splatting
3D Gaussian Splattingtaeseon ryu
 
Hyperbolic Image Embedding.pptx
Hyperbolic Image Embedding.pptxHyperbolic Image Embedding.pptx
Hyperbolic Image Embedding.pptxtaeseon ryu
 
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정taeseon ryu
 
LLaMA Open and Efficient Foundation Language Models - 230528.pdf
LLaMA Open and Efficient Foundation Language Models - 230528.pdfLLaMA Open and Efficient Foundation Language Models - 230528.pdf
LLaMA Open and Efficient Foundation Language Models - 230528.pdftaeseon ryu
 
Dataset Distillation by Matching Training Trajectories
Dataset Distillation by Matching Training Trajectories Dataset Distillation by Matching Training Trajectories
Dataset Distillation by Matching Training Trajectories taeseon ryu
 
Packed Levitated Marker for Entity and Relation Extraction
Packed Levitated Marker for Entity and Relation ExtractionPacked Levitated Marker for Entity and Relation Extraction
Packed Levitated Marker for Entity and Relation Extractiontaeseon ryu
 
MOReL: Model-Based Offline Reinforcement Learning
MOReL: Model-Based Offline Reinforcement LearningMOReL: Model-Based Offline Reinforcement Learning
MOReL: Model-Based Offline Reinforcement Learningtaeseon ryu
 
Scaling Instruction-Finetuned Language Models
Scaling Instruction-Finetuned Language ModelsScaling Instruction-Finetuned Language Models
Scaling Instruction-Finetuned Language Modelstaeseon ryu
 
Visual prompt tuning
Visual prompt tuningVisual prompt tuning
Visual prompt tuningtaeseon ryu
 
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdf
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdfvariBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdf
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdftaeseon ryu
 
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdf
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdfReinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdf
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdftaeseon ryu
 
The Forward-Forward Algorithm
The Forward-Forward AlgorithmThe Forward-Forward Algorithm
The Forward-Forward Algorithmtaeseon ryu
 
Towards Robust and Reproducible Active Learning using Neural Networks
Towards Robust and Reproducible Active Learning using Neural NetworksTowards Robust and Reproducible Active Learning using Neural Networks
Towards Robust and Reproducible Active Learning using Neural Networkstaeseon ryu
 
BRIO: Bringing Order to Abstractive Summarization
BRIO: Bringing Order to Abstractive SummarizationBRIO: Bringing Order to Abstractive Summarization
BRIO: Bringing Order to Abstractive Summarizationtaeseon ryu
 

More from taeseon ryu (20)

VoxelNet
VoxelNetVoxelNet
VoxelNet
 
OpineSum Entailment-based self-training for abstractive opinion summarization...
OpineSum Entailment-based self-training for abstractive opinion summarization...OpineSum Entailment-based self-training for abstractive opinion summarization...
OpineSum Entailment-based self-training for abstractive opinion summarization...
 
3D Gaussian Splatting
3D Gaussian Splatting3D Gaussian Splatting
3D Gaussian Splatting
 
JetsonTX2 Python
JetsonTX2 Python JetsonTX2 Python
JetsonTX2 Python
 
Hyperbolic Image Embedding.pptx
Hyperbolic Image Embedding.pptxHyperbolic Image Embedding.pptx
Hyperbolic Image Embedding.pptx
 
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정
MCSE_Multimodal Contrastive Learning of Sentence Embeddings_변현정
 
LLaMA Open and Efficient Foundation Language Models - 230528.pdf
LLaMA Open and Efficient Foundation Language Models - 230528.pdfLLaMA Open and Efficient Foundation Language Models - 230528.pdf
LLaMA Open and Efficient Foundation Language Models - 230528.pdf
 
YOLO V6
YOLO V6YOLO V6
YOLO V6
 
Dataset Distillation by Matching Training Trajectories
Dataset Distillation by Matching Training Trajectories Dataset Distillation by Matching Training Trajectories
Dataset Distillation by Matching Training Trajectories
 
RL_UpsideDown
RL_UpsideDownRL_UpsideDown
RL_UpsideDown
 
Packed Levitated Marker for Entity and Relation Extraction
Packed Levitated Marker for Entity and Relation ExtractionPacked Levitated Marker for Entity and Relation Extraction
Packed Levitated Marker for Entity and Relation Extraction
 
MOReL: Model-Based Offline Reinforcement Learning
MOReL: Model-Based Offline Reinforcement LearningMOReL: Model-Based Offline Reinforcement Learning
MOReL: Model-Based Offline Reinforcement Learning
 
Scaling Instruction-Finetuned Language Models
Scaling Instruction-Finetuned Language ModelsScaling Instruction-Finetuned Language Models
Scaling Instruction-Finetuned Language Models
 
Visual prompt tuning
Visual prompt tuningVisual prompt tuning
Visual prompt tuning
 
mPLUG
mPLUGmPLUG
mPLUG
 
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdf
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdfvariBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdf
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pdf
 
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdf
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdfReinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdf
Reinforced Genetic Algorithm Learning For Optimizing Computation Graphs.pdf
 
The Forward-Forward Algorithm
The Forward-Forward AlgorithmThe Forward-Forward Algorithm
The Forward-Forward Algorithm
 
Towards Robust and Reproducible Active Learning using Neural Networks
Towards Robust and Reproducible Active Learning using Neural NetworksTowards Robust and Reproducible Active Learning using Neural Networks
Towards Robust and Reproducible Active Learning using Neural Networks
 
BRIO: Bringing Order to Abstractive Summarization
BRIO: Bringing Order to Abstractive SummarizationBRIO: Bringing Order to Abstractive Summarization
BRIO: Bringing Order to Abstractive Summarization
 

Recently uploaded

Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |aasikanpl
 
Cytokinin, mechanism and its application.pptx
Cytokinin, mechanism and its application.pptxCytokinin, mechanism and its application.pptx
Cytokinin, mechanism and its application.pptxVarshiniMK
 
‏‏VIRUS - 123455555555555555555555555555555555555555
‏‏VIRUS - 123455555555555555555555555555555555555555‏‏VIRUS - 123455555555555555555555555555555555555555
‏‏VIRUS - 123455555555555555555555555555555555555555kikilily0909
 
Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptArshadWarsi13
 
insect anatomy and insect body wall and their physiology
insect anatomy and insect body wall and their physiologyinsect anatomy and insect body wall and their physiology
insect anatomy and insect body wall and their physiologyDrAnita Sharma
 
Solution chemistry, Moral and Normal solutions
Solution chemistry, Moral and Normal solutionsSolution chemistry, Moral and Normal solutions
Solution chemistry, Moral and Normal solutionsHajira Mahmood
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real timeGrafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real timeSatoshi NAKAHIRA
 
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxMicrophone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxpriyankatabhane
 
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.PraveenaKalaiselvan1
 
Heredity: Inheritance and Variation of Traits
Heredity: Inheritance and Variation of TraitsHeredity: Inheritance and Variation of Traits
Heredity: Inheritance and Variation of TraitsCharlene Llagas
 
Scheme-of-Work-Science-Stage-4 cambridge science.docx
Scheme-of-Work-Science-Stage-4 cambridge science.docxScheme-of-Work-Science-Stage-4 cambridge science.docx
Scheme-of-Work-Science-Stage-4 cambridge science.docxyaramohamed343013
 
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.aasikanpl
 
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tanta
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tantaDashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tanta
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tantaPraksha3
 
Forest laws, Indian forest laws, why they are important
Forest laws, Indian forest laws, why they are importantForest laws, Indian forest laws, why they are important
Forest laws, Indian forest laws, why they are importantadityabhardwaj282
 
Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2John Carlo Rollon
 
Module 4: Mendelian Genetics and Punnett Square
Module 4: Mendelian Genetics and Punnett SquareModule 4: Mendelian Genetics and Punnett Square
Module 4: Mendelian Genetics and Punnett SquareIsiahStephanRadaza
 
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...lizamodels9
 
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfAnalytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfSwapnil Therkar
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Patrick Diehl
 

Recently uploaded (20)

Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
Call Us ≽ 9953322196 ≼ Call Girls In Lajpat Nagar (Delhi) |
 
Cytokinin, mechanism and its application.pptx
Cytokinin, mechanism and its application.pptxCytokinin, mechanism and its application.pptx
Cytokinin, mechanism and its application.pptx
 
‏‏VIRUS - 123455555555555555555555555555555555555555
‏‏VIRUS - 123455555555555555555555555555555555555555‏‏VIRUS - 123455555555555555555555555555555555555555
‏‏VIRUS - 123455555555555555555555555555555555555555
 
Transposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.pptTransposable elements in prokaryotes.ppt
Transposable elements in prokaryotes.ppt
 
insect anatomy and insect body wall and their physiology
insect anatomy and insect body wall and their physiologyinsect anatomy and insect body wall and their physiology
insect anatomy and insect body wall and their physiology
 
Solution chemistry, Moral and Normal solutions
Solution chemistry, Moral and Normal solutionsSolution chemistry, Moral and Normal solutions
Solution chemistry, Moral and Normal solutions
 
Grafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real timeGrafana in space: Monitoring Japan's SLIM moon lander in real time
Grafana in space: Monitoring Japan's SLIM moon lander in real time
 
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxMicrophone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
 
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
BIOETHICS IN RECOMBINANT DNA TECHNOLOGY.
 
Heredity: Inheritance and Variation of Traits
Heredity: Inheritance and Variation of TraitsHeredity: Inheritance and Variation of Traits
Heredity: Inheritance and Variation of Traits
 
Scheme-of-Work-Science-Stage-4 cambridge science.docx
Scheme-of-Work-Science-Stage-4 cambridge science.docxScheme-of-Work-Science-Stage-4 cambridge science.docx
Scheme-of-Work-Science-Stage-4 cambridge science.docx
 
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.
Call Girls in Hauz Khas Delhi 💯Call Us 🔝9953322196🔝 💯Escort.
 
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tanta
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tantaDashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tanta
Dashanga agada a formulation of Agada tantra dealt in 3 Rd year bams agada tanta
 
Forest laws, Indian forest laws, why they are important
Forest laws, Indian forest laws, why they are importantForest laws, Indian forest laws, why they are important
Forest laws, Indian forest laws, why they are important
 
Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2Evidences of Evolution General Biology 2
Evidences of Evolution General Biology 2
 
Module 4: Mendelian Genetics and Punnett Square
Module 4: Mendelian Genetics and Punnett SquareModule 4: Mendelian Genetics and Punnett Square
Module 4: Mendelian Genetics and Punnett Square
 
Engler and Prantl system of classification in plant taxonomy
Engler and Prantl system of classification in plant taxonomyEngler and Prantl system of classification in plant taxonomy
Engler and Prantl system of classification in plant taxonomy
 
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...
Best Call Girls In Sector 29 Gurgaon❤️8860477959 EscorTs Service In 24/7 Delh...
 
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdfAnalytical Profile of Coleus Forskohlii | Forskolin .pdf
Analytical Profile of Coleus Forskohlii | Forskolin .pdf
 
Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?Is RISC-V ready for HPC workload? Maybe?
Is RISC-V ready for HPC workload? Maybe?
 

Style gan2 review

  • 1. Analyzing and Improving the Image Quality of StyleGAN 펀디멘탈 팀: 송헌, 고형권, 김동희, 김창연, 이민경, 이재윤 발표: 고형권 (hyungkwonko@gmail.com)
  • 2. Progressive GAN (2018 ICLR) recap Image from original paper 2
  • 3. Image from original paper StyleGAN (2019 CVPR) recap 3
  • 4. Image from original paper StyleGAN (2019 CVPR) recap 4
  • 5. Image from original paper StyleGAN (2019 CVPR) recap 5
  • 8. StyleGAN2 (2020 CVPR) ● In this paper, we focus all analysis solely on W, as it is the relevant latent space from the synthesis network’s point of view. 8
  • 9. StyleGAN2 (2020 CVPR) Image from original paper 9
  • 10. Image from original paper The Problem of StyleGAN (Droplet artifacts) 10
  • 11. The Problem of StyleGAN (Droplet artifacts) https://www.youtube.com/watch?v=c-NJtV9Jvp0 11
  • 12. B. Weight Demodulation ● AdaIN operation normalizes the mean and variance of each feature map separately, thereby potentially destroying any information found in the magnitudes of the features relative to each other. ● How to fix? Get rid of AdaIN structure 12
  • 13. B. Weight Demodulation ● BEFORE (StyleGAN) ● W → affine transform → A Image from original paper 13
  • 14. B. Weight Demodulation ● AFTER (StyleGAN2) Image from original paper 14
  • 15. B. Weight Demodulation Image from original paper 15
  • 16. The Result of Weight Demodulation https://www.youtube.com/watch?v=c-NJtV9Jvp0 16
  • 17. The Result of Weight Demodulation (in style mixing) https://www.youtube.com/watch?v=c-NJtV9Jvp0 17
  • 18. Q & A
  • 19. Perceptual Path Length score ● Perceptual Path Length score (PPL): Sampled from latent-space vectors using interpolation → distance calculation using features of VGG Image from original paper 19
  • 20. Perceptual Path Length score Image from original paper 20
  • 21. Why LOW PPL == GOOD Quality? ● NOT immediately obvious… ● Hypothesis: GOOD image region (STRETCHED, large latent space) BAD image region (SQUEEZED, small latent space) Image from original paper 21
  • 23. Why better than FID and Precision & Recall? Geirhos et al., 2019 ICLR (oral) 23
  • 24. D. Path Length Regularization ● We would like to encourage that a fixed-size step in W results in a non-zero, fixed-magnitude change in the image. ● We can measure the deviation from this ideal empirically by stepping into random directions in the image space and observing the corresponding w gradients. Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow. Is generator conditioning causally related to GAN performance? CoRR, abs/1802.08768, 2018. 24
  • 25. C. Lazy Regularization ● As the resulting regularization term is somewhat expensive to compute, we first describe a general optimization that applies to any regularization technique. ● … performed only once every 16 mini-batches 25
  • 26. Q & A
  • 27. The Problem of StyleGAN (Phase artifacts) Image from original paper 27
  • 28. The Problem of StyleGAN (Phase artifacts) https://www.youtube.com/watch?v=c-NJtV9Jvp0 28
  • 29. The Problem of StyleGAN (Phase artifacts) ● … We believe the problem is that in progressive growing each resolution serves momentarily as the output resolution, forcing it to generate maximal frequency details, which then leads to the trained network to have excessively high frequencies in the intermediate layers, ... 29
  • 30. MSG-GAN (2020 CVPR) Image from original paper 30
  • 31. E. No Growing, New G & D Architecture Image from original paper 31
  • 32. E. No Growing, New G & D Architecture Image from original paper 32
  • 33. The Result of New Architecture https://www.youtube.com/watch?v=c-NJtV9Jvp0 33
  • 34. F. Large Networks for Better Performance … To verify this, we inspected the generated images manually and noticed that they generally lack some of the pixel-level detail... Image from original paper 34
  • 35. F. Large Networks for Better Performance Image from original paper 35
  • 36. Deep Fake Detection Algorithm ● How can we detect generated / synthesized image? Image from original paper 36
  • 37. Deep Fake Detection Algorithm https://www.youtube.com/watch?v=c-NJtV9Jvp0 37
  • 38. How to do this? LPIPS distance Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 586-595). 38
  • 39. How to do this? ● (for StyleGAN) it appears that the mapping from W to images is too complex for this to succeed reliably in practice. ● We find it encouraging that StyleGAN2 makes source attribution easier even though the image quality has improved significantly. Image from original paper 39
  • 42. Q & A