SlideShare une entreprise Scribd logo

Generative Adversarial Networks (GANs)

•Télécharger en tant que PPTX, PDF•
•
A
Amol Patil

Introduction to GANs Generator & Discriminator Networks GAN Schema / GAN Lab Generative Models Face Generation - Vanilla GAN, DCGAN, CoGAN, ProGAN, StyleGAN, BigGAN Style Transfer - CGAN, pix2pix Image to Image Translation (CycleGAN) Video Synthesis (vid2vid, Everybody Dance Now) Doodle to Realistic Landscape (SPADE, GauGAN) Image Super Resolution (ISR - ESRGAN) Colorize/Restore Images (Deoldify - NoGAN)

Technologie
1  sur  18
Generative Adversarial Networks Amol Patil July 15, 2019
Overview DNN Architecture Pioneered by Dr. Ian Goodfellow & his coworkers in 2014. The ability to synthesize artificial samples (Images, Speech, Text, Videos) that are indistinguishable from real world is very exciting !! “GANs is the most interesting idea in the last 10 years in Machine Learning” — Yann LeCun, Director of AI Research @Facebook AI. It consists of two NNs (Generator and Discriminator) competing with each other until both networks are experts.
Generator & Discriminator Networks https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Generator Discriminator
GAN Schema / GAN Lab GAN Lab - Train GANs in browser, TF based https://poloclub.github.io/ganlab/ https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431
Make ML Work - Ian Goodfellow@ICLR 2019 ● Generative Models ○ Sample Generation (Face Generation - GAN to BigGAN) ○ Image Translation (Unsupervised - CGAN - pix2pix, CycleGAN) ○ Video to Video Synthesis (vid2vid, Everybody Dance Now) ○ Photorealistic Expression (GauGAN, SPADE) ○ GANufacturing (Physical 3D printed dental crown) ○ New area - GANs for Fashion ● Security (Adversarial training for robust classifiers) ● Model-based Optimization (Design DNA to optimize protein) ● Reinforcement Learning (Self-Play) ● Extreme Reliability (Robustness - Air traffic control, Surgery robot) ● Label efficiency (Multiple outcomes from discriminator) ● Domain Adoption (Person ReID, Eye samples, Robots training, Sim - Real) ● Fairness, Accountability and Transparency (Improving interpretability) ● Neuroscience (More understanding of how brain works) https://www.youtube.com/watch?v=sucqskXRkss
GAN Progress on Face Generation GAN DCGAN CoGAN ProGAN StyleGAN Checkout - This Person Does Not Existhttps://twitter.com/goodfellow_ian/status/1084973596236144640?lang=en
ProGAN Breakthrough with NVIDIA’s ProGAN progressive training – it starts by training the generator and the discriminator with a very low resolution image (e.g. 4×4) and adds a higher resolution layer every time [0 to 14 days for 1024x1024] https://towardsdatascience.com/progan-how-nvidia-generated-images-of-unprecedented-quality-51c98ec2cbd2
StyleGAN technique for generating high quality, realistic images. Control different visual features of the image based on resolution Face Generation - 1. Coarse – resolution of up to 8x8 – affects pose, general hair style, face shape etc 2. Middle – resolution of 16x16 to 32x32 – affects finer facial features, hair style, eyes open/closed, etc. 3. Fine – resolution of 64x64 to 1024x1024 – affects color scheme (eye, hair and skin) & micro features. StyleGAN Encoder https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/
BigGAN Training GAN on large scale (JFT-300 300 M ImageNet like database of images) on TFU cluster. BigGAN could do what ProGAN thought would require multi-scale approach in single-scale by using different techniques - truncation trick, ResNet bottleneck, careful experimentation. BigGAN completely obliterates the previous state of the art Inception score of 52.52 with a whopping score of 152.8. https://arxiv.org/abs/1809.11096v2, https://blog.floydhub.com/gans-story-so-far/
pix2pix - Conditional GAN https://github.com/phillipi/pix2pix
CycleGAN - Image to Image Translation Uses double mapping i.e. two-step transformation of source domain image - first by trying to map it to target domain and then back to the original image. Hence, we don’t need to explicitly give target domain image https://github.com/junyanz/CycleGAN
vid2vid - Everybody Dance Now! https://github.com/NVIDIA/vid2vid
Doodles to Photorealistic Landscapes GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers. http://nvidia-research-mingyuliu.com/gaugan
Image Super Resolution (ISR - ESRGAN) Before - 256x256 https://www.cityofhope.org/image/meals-256x256.jpg After -512x512 https://github.com/idealo/image-super-resolution
Image Super Resolution (ESRGAN) Before - Compressed 256x256 After- 512x512
Colorize & Restore old Images and Videos (NoGAN) https://github.com/jantic/DeOldify
Thank You!
GAN Architectures Vanilla GAN Conditional GAN (CGAN) Deep Convolutional GAN (DCGAN) Laplacian Pyramid GAN (LAPGAN) Wasserstein GAN (WGAN) Super Resolution GAN (SRGAN) - Progressive GAN (ProGAN) StyleGAN Everybody Dance Now PetSwap BigGAN https://www.geeksforgeeks.org/generative-adversarial-network-gan/

Recommandé

Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Introduction to Generative Adversarial Networks (GANs)
Introduction to Generative Adversarial Networks (GANs)Introduction to Generative Adversarial Networks (GANs)
Introduction to Generative Adversarial Networks (GANs)Appsilon Data Science
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMustafa Yagmur
 
Evolution of the StyleGAN family
Evolution of the StyleGAN familyEvolution of the StyleGAN family
Evolution of the StyleGAN familyVitaly Bondar
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
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
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Manohar Mukku
 

Recommandé

Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Introduction to Generative Adversarial Networks (GANs)
Introduction to Generative Adversarial Networks (GANs)Introduction to Generative Adversarial Networks (GANs)
Introduction to Generative Adversarial Networks (GANs)Appsilon Data Science
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMustafa Yagmur
 
Evolution of the StyleGAN family
Evolution of the StyleGAN familyEvolution of the StyleGAN family
Evolution of the StyleGAN familyVitaly Bondar
 
GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
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
 
Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN)Manohar Mukku
 
GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptxMAHMOUD729246
 
Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...Universitat Politècnica de Catalunya
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
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 Network (GAN)
Generative Adversarial Network (GAN)Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Prakhar Rastogi
 
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Universitat Politècnica de Catalunya
 
Explicit Density Models
Explicit Density ModelsExplicit Density Models
Explicit Density ModelsSangwoo Mo
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsArtifacia
 
Style gan
Style ganStyle gan
Style gan哲东 郑
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGANNAVER Engineering
 
Introduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANsIntroduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANsHichem Felouat
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models Chia-Wen Cheng
 
[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You Need[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You NeedDaiki Tanaka
 
A Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial NetworksA Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial NetworksJong Wook Kim
 
Simple Introduction to AutoEncoder
Simple Introduction to AutoEncoderSimple Introduction to AutoEncoder
Simple Introduction to AutoEncoderJun Lang
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNNNoura Hussein
 
Gan intro
Gan introGan intro
Gan introHyungjoo Cho
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 
Null
NullNull
NullEraylsonGaldino1
 
Challenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingChallenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
 

Contenu connexe

Tendances

GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptxMAHMOUD729246
 
Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...Universitat Politècnica de Catalunya
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
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 Network (GAN)
Generative Adversarial Network (GAN)Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Prakhar Rastogi
 
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Universitat Politècnica de Catalunya
 
Explicit Density Models
Explicit Density ModelsExplicit Density Models
Explicit Density ModelsSangwoo Mo
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsArtifacia
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGANNAVER Engineering
 
Introduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANsIntroduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANsHichem Felouat
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models Chia-Wen Cheng
 
[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You Need[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You NeedDaiki Tanaka
 
A Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial NetworksA Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial NetworksJong Wook Kim
 
Simple Introduction to AutoEncoder
Simple Introduction to AutoEncoderSimple Introduction to AutoEncoder
Simple Introduction to AutoEncoderJun Lang
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNNNoura Hussein
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 

Tendances (20)

GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and Applications
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptx
 
Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...Deep Learning for Computer Vision: Generative models and adversarial training...
Deep Learning for Computer Vision: Generative models and adversarial training...
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial Networks
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
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)
 
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
 
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)
 
Explicit Density Models
Explicit Density ModelsExplicit Density Models
Explicit Density Models
 
Generative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their ApplicationsGenerative Adversarial Networks and Their Applications
Generative Adversarial Networks and Their Applications
 
Style gan
Style ganStyle gan
Style gan
 
Finding connections among images using CycleGAN
Finding connections among images using CycleGANFinding connections among images using CycleGAN
Finding connections among images using CycleGAN
 
Introduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANsIntroduction To Generative Adversarial Networks GANs
Introduction To Generative Adversarial Networks GANs
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models
 
[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You Need[Paper Reading] Attention is All You Need
[Paper Reading] Attention is All You Need
 
A Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial NetworksA Short Introduction to Generative Adversarial Networks
A Short Introduction to Generative Adversarial Networks
 
Simple Introduction to AutoEncoder
Simple Introduction to AutoEncoderSimple Introduction to AutoEncoder
Simple Introduction to AutoEncoder
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNN
 
Gan intro
Gan introGan intro
Gan intro
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial Networks
 

Similaire à Generative Adversarial Networks (GANs)

Challenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingChallenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
 
IRJET- A Study of Generative Adversarial Networks in 3D Modelling
IRJET- A Study of Generative Adversarial Networks in 3D ModellingIRJET- A Study of Generative Adversarial Networks in 3D Modelling
IRJET- A Study of Generative Adversarial Networks in 3D ModellingIRJET Journal
 
The power of deep learning models applications
The power of deep learning models applicationsThe power of deep learning models applications
The power of deep learning models applicationsSameera Sk
 
Dog Breed Prediction System (Web)
Dog Breed Prediction System (Web)Dog Breed Prediction System (Web)
Dog Breed Prediction System (Web)IRJET Journal
 
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET Journal
 
The power of_deep_learning_models_applications
The power of_deep_learning_models_applicationsThe power of_deep_learning_models_applications
The power of_deep_learning_models_applicationsDrjabez
 
Deep Learning Applications and Image Processing
Deep Learning Applications and Image ProcessingDeep Learning Applications and Image Processing
Deep Learning Applications and Image Processingijtsrd
 
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGHANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
 
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGHANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGIRJET Journal
 
A Literature Survey on Image Linguistic Visual Question Answering
A Literature Survey on Image Linguistic Visual Question AnsweringA Literature Survey on Image Linguistic Visual Question Answering
A Literature Survey on Image Linguistic Visual Question AnsweringIRJET Journal
 
An Extensive Review on Generative Adversarial Networks GAN’s
An Extensive Review on Generative Adversarial Networks GAN’sAn Extensive Review on Generative Adversarial Networks GAN’s
An Extensive Review on Generative Adversarial Networks GAN’sijtsrd
 
IRJET- Car Defect Detection using Machine Learning for Insurance
IRJET- Car Defect Detection using Machine Learning for InsuranceIRJET- Car Defect Detection using Machine Learning for Insurance
IRJET- Car Defect Detection using Machine Learning for InsuranceIRJET Journal
 
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...Codemotion
 
Data Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA
 
Ramesh - Resume
Ramesh - ResumeRamesh - Resume
Ramesh - ResumeRAMESH C
 
Fashion AI Literature
Fashion AI LiteratureFashion AI Literature
Fashion AI Literatureijtsrd
 
System for Detecting Deepfake in Videos – A Survey
System for Detecting Deepfake in Videos – A SurveySystem for Detecting Deepfake in Videos – A Survey
System for Detecting Deepfake in Videos – A SurveyIRJET Journal
 
Creating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersCreating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersIRJET Journal
 
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...IRJET Journal
 

Similaire à Generative Adversarial Networks (GANs) (20)

Null
NullNull
Null
 
Challenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingChallenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
Challenges of Deep Learning in Computer Vision Webinar - Tessellate Imaging
 
IRJET- A Study of Generative Adversarial Networks in 3D Modelling
IRJET- A Study of Generative Adversarial Networks in 3D ModellingIRJET- A Study of Generative Adversarial Networks in 3D Modelling
IRJET- A Study of Generative Adversarial Networks in 3D Modelling
 
The power of deep learning models applications
The power of deep learning models applicationsThe power of deep learning models applications
The power of deep learning models applications
 
Dog Breed Prediction System (Web)
Dog Breed Prediction System (Web)Dog Breed Prediction System (Web)
Dog Breed Prediction System (Web)
 
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial NetworkIRJET- Generating 3D Models Using 3D Generative Adversarial Network
IRJET- Generating 3D Models Using 3D Generative Adversarial Network
 
The power of_deep_learning_models_applications
The power of_deep_learning_models_applicationsThe power of_deep_learning_models_applications
The power of_deep_learning_models_applications
 
Deep Learning Applications and Image Processing
Deep Learning Applications and Image ProcessingDeep Learning Applications and Image Processing
Deep Learning Applications and Image Processing
 
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGHANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
 
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNINGHANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
HANDWRITTEN DIGIT RECOGNITION USING MACHINE LEARNING
 
A Literature Survey on Image Linguistic Visual Question Answering
A Literature Survey on Image Linguistic Visual Question AnsweringA Literature Survey on Image Linguistic Visual Question Answering
A Literature Survey on Image Linguistic Visual Question Answering
 
An Extensive Review on Generative Adversarial Networks GAN’s
An Extensive Review on Generative Adversarial Networks GAN’sAn Extensive Review on Generative Adversarial Networks GAN’s
An Extensive Review on Generative Adversarial Networks GAN’s
 
IRJET- Car Defect Detection using Machine Learning for Insurance
IRJET- Car Defect Detection using Machine Learning for InsuranceIRJET- Car Defect Detection using Machine Learning for Insurance
IRJET- Car Defect Detection using Machine Learning for Insurance
 
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...
 
Data Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase Lexset
 
Ramesh - Resume
Ramesh - ResumeRamesh - Resume
Ramesh - Resume
 
Fashion AI Literature
Fashion AI LiteratureFashion AI Literature
Fashion AI Literature
 
System for Detecting Deepfake in Videos – A Survey
System for Detecting Deepfake in Videos – A SurveySystem for Detecting Deepfake in Videos – A Survey
System for Detecting Deepfake in Videos – A Survey
 
Creating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and AutoencodersCreating Objects for Metaverse using GANs and Autoencoders
Creating Objects for Metaverse using GANs and Autoencoders
 
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...
An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...
 

Dernier

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessPixlogix Infotech
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 

Dernier (20)

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 

Generative Adversarial Networks (GANs)

  • 2. Overview DNN Architecture Pioneered by Dr. Ian Goodfellow & his coworkers in 2014. The ability to synthesize artificial samples (Images, Speech, Text, Videos) that are indistinguishable from real world is very exciting !! “GANs is the most interesting idea in the last 10 years in Machine Learning” — Yann LeCun, Director of AI Research @Facebook AI. It consists of two NNs (Generator and Discriminator) competing with each other until both networks are experts.
  • 3. Generator & Discriminator Networks https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7 Generator Discriminator
  • 4. GAN Schema / GAN Lab GAN Lab - Train GANs in browser, TF based https://poloclub.github.io/ganlab/ https://towardsdatascience.com/explained-a-style-based-generator-architecture-for-gans-generating-and-tuning-realistic-6cb2be0f431
  • 5. Make ML Work - Ian Goodfellow@ICLR 2019 ● Generative Models ○ Sample Generation (Face Generation - GAN to BigGAN) ○ Image Translation (Unsupervised - CGAN - pix2pix, CycleGAN) ○ Video to Video Synthesis (vid2vid, Everybody Dance Now) ○ Photorealistic Expression (GauGAN, SPADE) ○ GANufacturing (Physical 3D printed dental crown) ○ New area - GANs for Fashion ● Security (Adversarial training for robust classifiers) ● Model-based Optimization (Design DNA to optimize protein) ● Reinforcement Learning (Self-Play) ● Extreme Reliability (Robustness - Air traffic control, Surgery robot) ● Label efficiency (Multiple outcomes from discriminator) ● Domain Adoption (Person ReID, Eye samples, Robots training, Sim - Real) ● Fairness, Accountability and Transparency (Improving interpretability) ● Neuroscience (More understanding of how brain works) https://www.youtube.com/watch?v=sucqskXRkss
  • 6. GAN Progress on Face Generation GAN DCGAN CoGAN ProGAN StyleGAN Checkout - This Person Does Not Existhttps://twitter.com/goodfellow_ian/status/1084973596236144640?lang=en
  • 7. ProGAN Breakthrough with NVIDIA’s ProGAN progressive training – it starts by training the generator and the discriminator with a very low resolution image (e.g. 4×4) and adds a higher resolution layer every time [0 to 14 days for 1024x1024] https://towardsdatascience.com/progan-how-nvidia-generated-images-of-unprecedented-quality-51c98ec2cbd2
  • 8. StyleGAN technique for generating high quality, realistic images. Control different visual features of the image based on resolution Face Generation - 1. Coarse – resolution of up to 8x8 – affects pose, general hair style, face shape etc 2. Middle – resolution of 16x16 to 32x32 – affects finer facial features, hair style, eyes open/closed, etc. 3. Fine – resolution of 64x64 to 1024x1024 – affects color scheme (eye, hair and skin) & micro features. StyleGAN Encoder https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/
  • 9. BigGAN Training GAN on large scale (JFT-300 300 M ImageNet like database of images) on TFU cluster. BigGAN could do what ProGAN thought would require multi-scale approach in single-scale by using different techniques - truncation trick, ResNet bottleneck, careful experimentation. BigGAN completely obliterates the previous state of the art Inception score of 52.52 with a whopping score of 152.8. https://arxiv.org/abs/1809.11096v2, https://blog.floydhub.com/gans-story-so-far/
  • 10. pix2pix - Conditional GAN https://github.com/phillipi/pix2pix
  • 11. CycleGAN - Image to Image Translation Uses double mapping i.e. two-step transformation of source domain image - first by trying to map it to target domain and then back to the original image. Hence, we don’t need to explicitly give target domain image https://github.com/junyanz/CycleGAN
  • 12. vid2vid - Everybody Dance Now! https://github.com/NVIDIA/vid2vid
  • 13. Doodles to Photorealistic Landscapes GauGAN could offer a powerful tool for creating virtual worlds to everyone from architects and urban planners to landscape designers and game developers. http://nvidia-research-mingyuliu.com/gaugan
  • 14. Image Super Resolution (ISR - ESRGAN) Before - 256x256 https://www.cityofhope.org/image/meals-256x256.jpg After -512x512 https://github.com/idealo/image-super-resolution
  • 15. Image Super Resolution (ESRGAN) Before - Compressed 256x256 After- 512x512
  • 16. Colorize & Restore old Images and Videos (NoGAN) https://github.com/jantic/DeOldify
  • 18. GAN Architectures Vanilla GAN Conditional GAN (CGAN) Deep Convolutional GAN (DCGAN) Laplacian Pyramid GAN (LAPGAN) Wasserstein GAN (WGAN) Super Resolution GAN (SRGAN) - Progressive GAN (ProGAN) StyleGAN Everybody Dance Now PetSwap BigGAN https://www.geeksforgeeks.org/generative-adversarial-network-gan/

Notes de l'éditeur

  1. Generative models allow a computer to create data — like photos, movies or music — by itself. Build understanding of real world objects, Generate Stock Images, Entire Movie, Video Game, Music, New Fonts Apple Hires The GANfather Ian Goodfellow Away From Google To Up Its ...
  2. Printing Fake Notes - Counterfeiter (forgery) Gradient Ascent, Police Officer Gradient Descent This back-and-forth game between the Generator and the Discriminator continues thousands of times until both networks are experts. Two adversaries are in constant battle throughout the training process