SlideShare une entreprise Scribd logo

Introduction to Generative Adversarial Networks (GANs)

•
•
Appsilon Data Science
Appsilon Data Science

Introduction to Generative Adversarial Networks (GANs) by Michał Maj Full story: https://appsilon.com/satellite-imagery-generation-with-gans/

Technologie
1  sur  13
Télécharger pour lire hors ligne
Soft introduction to Generative Adversarial Networks (GANs) Appsilon tech talks | Michał Maj | 24/10/2018
Agenda Agenda: ● Difference between discriminative and generative algorithms. ● What are GANs ? ● Use cases of GANs. ● Building Vanilla DCGAN in Keras. ● Why it is hard to train a GAN ?
Discriminative and generative algorithms Let’s start with a ConvNet for satellite imagery classification (from previous tech talk). As you can remember our task looked like this: we wanted to predict class (ship or non-ship), or to be more specific the probability that the image belongs to the specific class, given the image. Each image was composed of a set of pixels that we were using as features / inputs. Mathematically speaking we were using set of features X (pixels) o get the conditional probability of Y given X: p(y|x) This is an example of discriminative algorithm.
Discriminative and generative algorithms Generative algorithms on the other hand are doing something completely opposite. In our example, assuming that the class of an image is ship how should the image look like, so what value should each pixel have? This time we’re generating distribution of X (pixels) given Y(class) or joint probability: p(x|y) or p(x,y)
What are GANs ? Generative adversarial networks, introduced in 2014 by Ian Goodfellow, are generative algorithms comprised of two deep neural networks “playing” a zero-sum game against each other. There are many different “flavours” of GANs and depending on the task we will use different networks to build our GAN. For example DCGAN (Deep Convolutional Generative Adversarial Networks) is a GAN designed for basic image generation.
What are GANs ? First network is called generator and it’s basically responsible for creating new instances of data from random noise. Second network is called discriminator and it “judges” if the data generated by generator is real or fake having real data to compare.
Use cases of GANs ● Data augmentation - e.g. create new instances of data to train your model, alternative to bayesian methods like Gibbs sampling “NVIDIA showed amazing example of this approach in action: they used GANs to augment dataset of medical brain CT images with different diseases and showed that the classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity.”
Use cases of GANs ● Image generation - e.g. Disney is generating new textures for animations ● Anomaly detection - GAN learns the predictor distribution so it can detect outliers and anomalies ● Domain adaptation Source: https://www.google.com/url?q=https://medium.com/@alexrachnog/gans-beyond-generation-7-alternative-use-cases-725c60ba95e8?fbclid%3DIwAR2c81U-rmlb3FFWq_s56dafuCV-tvOgjqHtc Ot0KAwm34I8Z3JJHX6XHZk&sa=D&ust=1547052892635000&usg=AFQjCNFWNEG79Y71yW3V9JVMcH3JOhoK2A
Use cases of GANs ● Data manipulation Source: https://www.google.com/url?q=https://medium.com/@alexrachnog/gans-beyond-generation-7-alternative-use-cases-725c60ba95e8?fbclid%3DIwAR2c81U-rmlb3FFWq_s56dafuCV-tvOgjqHtcOt0KAwm34I8Z3JJHX6XHZk&sa =D&ust=1547052892635000&usg=AFQjCNFWNEG79Y71yW3V9JVMcH3JOhoK2A https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09
Building Vanilla DCGAN in Keras
Why it is hard to train a GAN ● There’s no fixed minimum of a loss function (it changes in each iteration) ● Discriminator gets too successful (vanishing gradient problem for generator) ● Complicated goal: Source: https://medium.com/@jonathan_hui/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b
Resources https://poloclub.github.io/ganlab/ https://medium.com/@alexrachnog/gans-beyond-generation- 7-alternative-use-cases-725c60ba95e8?fbclid=IwAR2c81U-r mlb3FFWq_s56dafuCV-tvOgjqHtcOt0KAwm34I8Z3JJHX6XH Zk https://medium.com/@jonathan_hui/gan-whats-generative-ad versarial-networks-and-its-application-f39ed278ef09
Questions?

Recommandé

GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi
 
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
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Prakhar Rastogi
 
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
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMustafa Yagmur
 

Recommandé

GANs and Applications
GANs and ApplicationsGANs and Applications
GANs and ApplicationsHoang Nguyen
 
GAN - Theory and Applications
GAN - Theory and ApplicationsGAN - Theory and Applications
GAN - Theory and ApplicationsEmanuele Ghelfi
 
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
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks남주 김
 
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)Prakhar Rastogi
 
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
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMustafa Yagmur
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Amol Patil
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksBennoG1
 
Generative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical ImagingGenerative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical ImagingSanghoon Hong
 
Transfer Learning
Transfer LearningTransfer Learning
Transfer LearningHichem Felouat
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptxMAHMOUD729246
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning ExplainedMelanie Swan
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksChristian Perone
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network Yan Xu
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models Chia-Wen Cheng
 
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
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료종현 최
 
CNN Machine learning DeepLearning
CNN Machine learning DeepLearningCNN Machine learning DeepLearning
CNN Machine learning DeepLearningAbhishek Sharma
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual IntroductionLukas Masuch
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning David Voyles
 
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Universitat Politècnica de Catalunya
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNNNoura Hussein
 
GAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptxGAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptxRMDAcademicCoordinat
 
Generative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdfGenerative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdfMatthewHaws4
 

Contenu connexe

Tendances

Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksYunjey Choi
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial NetworksDong Heon Cho
 
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Amol Patil
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networksDing Li
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksBennoG1
 
Generative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical ImagingGenerative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical ImagingSanghoon Hong
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptxMAHMOUD729246
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning ExplainedMelanie Swan
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksChristian Perone
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial NetworksMark Chang
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network Yan Xu
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models Chia-Wen Cheng
 
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
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료종현 최
 
CNN Machine learning DeepLearning
CNN Machine learning DeepLearningCNN Machine learning DeepLearning
CNN Machine learning DeepLearningAbhishek Sharma
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual IntroductionLukas Masuch
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning David Voyles
 
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Universitat Politècnica de Catalunya
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNNNoura Hussein
 

Tendances (20)

Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Basic Generative Adversarial Networks
Basic Generative Adversarial NetworksBasic Generative Adversarial Networks
Basic Generative Adversarial Networks
 
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)
 
Generative adversarial networks
Generative adversarial networksGenerative adversarial networks
Generative adversarial networks
 
Introduction to Generative Adversarial Networks
Introduction to Generative Adversarial NetworksIntroduction to Generative Adversarial Networks
Introduction to Generative Adversarial Networks
 
Generative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical ImagingGenerative Adversarial Networks and Their Applications in Medical Imaging
Generative Adversarial Networks and Their Applications in Medical Imaging
 
Transfer Learning
Transfer LearningTransfer Learning
Transfer Learning
 
GANs Presentation.pptx
GANs Presentation.pptxGANs Presentation.pptx
GANs Presentation.pptx
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning Explained
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
 
Generative Adversarial Networks
Generative Adversarial NetworksGenerative Adversarial Networks
Generative Adversarial Networks
 
Convolutional neural network
Convolutional neural network Convolutional neural network
Convolutional neural network
 
Deep Generative Models
Deep Generative Models Deep Generative Models
Deep Generative Models
 
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
 
Gan 발표자료
Gan 발표자료Gan 발표자료
Gan 발표자료
 
CNN Machine learning DeepLearning
CNN Machine learning DeepLearningCNN Machine learning DeepLearning
CNN Machine learning DeepLearning
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
 
Intro to deep learning
Intro to deep learning Intro to deep learning
Intro to deep learning
 
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
 
Image classification using CNN
Image classification using CNNImage classification using CNN
Image classification using CNN
 

Similaire à Introduction to Generative Adversarial Networks (GANs)

GAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptxGAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptxRMDAcademicCoordinat
 
Generative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdfGenerative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdfMatthewHaws4
 
Face-GAN project report.pptx
Face-GAN project report.pptxFace-GAN project report.pptx
Face-GAN project report.pptxAndleebFatima16
 
Generative Adversarial Network (GAN) for Image Synthesis
Generative Adversarial Network (GAN) for Image SynthesisGenerative Adversarial Network (GAN) for Image Synthesis
Generative Adversarial Network (GAN) for Image SynthesisRiwaz Mahat
 
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
 
GNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptGNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptManiMaran230751
 
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
 
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
 
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
 
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...Kuan-Tsae Huang
 
Learning from Simulated and Unsupervised Images through Adversarial Training....
Learning from Simulated and Unsupervised Images through Adversarial Training....Learning from Simulated and Unsupervised Images through Adversarial Training....
Learning from Simulated and Unsupervised Images through Adversarial Training....eraser Juan José Calderón
 
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...Catalina Arango
 
Generative Adversarial Network (GANs).
Generative Adversarial Network (GANs).Generative Adversarial Network (GANs).
Generative Adversarial Network (GANs).kgandham169
 
Reading group gan - 20170417
Reading group gan - 20170417Reading group gan - 20170417
Reading group gan - 20170417Shuai Zhang
 
IMAGE GENERATION FROM CAPTION
IMAGE GENERATION FROM CAPTIONIMAGE GENERATION FROM CAPTION
IMAGE GENERATION FROM CAPTIONijscai
 
Image Generation from Caption
Image Generation from Caption Image Generation from Caption
Image Generation from Caption IJSCAI Journal
 
Generative advesarial networks technical seminar
Generative advesarial networks technical seminarGenerative advesarial networks technical seminar
Generative advesarial networks technical seminarkarthikmudaliar20
 

Similaire à Introduction to Generative Adversarial Networks (GANs) (20)

GAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptxGAN Deep Learning Approaches to Image Processing Applications (1).pptx
GAN Deep Learning Approaches to Image Processing Applications (1).pptx
 
Generative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdfGenerative Adversarial Networks GANs.pdf
Generative Adversarial Networks GANs.pdf
 
Face-GAN project report.pptx
Face-GAN project report.pptxFace-GAN project report.pptx
Face-GAN project report.pptx
 
Face-GAN project report
Face-GAN project reportFace-GAN project report
Face-GAN project report
 
Generative Adversarial Network (GAN) for Image Synthesis
Generative Adversarial Network (GAN) for Image SynthesisGenerative Adversarial Network (GAN) for Image Synthesis
Generative Adversarial Network (GAN) for Image Synthesis
 
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...
 
GNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.pptGNA 13552928 deep learning for GAN a.ppt
GNA 13552928 deep learning for GAN a.ppt
 
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
 
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
 
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...
 
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
 
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...
TWCC22_PPT_v3_KL.pdf quantum computer, quantum computer , quantum computer ,...
 
Learning from Simulated and Unsupervised Images through Adversarial Training....
Learning from Simulated and Unsupervised Images through Adversarial Training....Learning from Simulated and Unsupervised Images through Adversarial Training....
Learning from Simulated and Unsupervised Images through Adversarial Training....
 
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
 
GAN.pptx
GAN.pptxGAN.pptx
GAN.pptx
 
Generative Adversarial Network (GANs).
Generative Adversarial Network (GANs).Generative Adversarial Network (GANs).
Generative Adversarial Network (GANs).
 
Reading group gan - 20170417
Reading group gan - 20170417Reading group gan - 20170417
Reading group gan - 20170417
 
IMAGE GENERATION FROM CAPTION
IMAGE GENERATION FROM CAPTIONIMAGE GENERATION FROM CAPTION
IMAGE GENERATION FROM CAPTION
 
Image Generation from Caption
Image Generation from Caption Image Generation from Caption
Image Generation from Caption
 
Generative advesarial networks technical seminar
Generative advesarial networks technical seminarGenerative advesarial networks technical seminar
Generative advesarial networks technical seminar
 

Plus de Appsilon Data Science

User! 2019 best practices for building shiny enterprise applications
User! 2019 best practices for building shiny enterprise applicationsUser! 2019 best practices for building shiny enterprise applications
User! 2019 best practices for building shiny enterprise applicationsAppsilon Data Science
 
Styling your projects! leveraging css and r sass in r projects
Styling your projects! leveraging css and r sass in r projectsStyling your projects! leveraging css and r sass in r projects
Styling your projects! leveraging css and r sass in r projectsAppsilon Data Science
 
Successful Machine Learning projects in Fintech
Successful Machine Learning projects in FintechSuccessful Machine Learning projects in Fintech
Successful Machine Learning projects in FintechAppsilon Data Science
 
Tech Talk - Overview of Dash framework for building dashboards
Tech Talk - Overview of Dash framework for building dashboardsTech Talk - Overview of Dash framework for building dashboards
Tech Talk - Overview of Dash framework for building dashboardsAppsilon Data Science
 
Tech talk - Data Validation with assertr
Tech talk - Data Validation with assertrTech talk - Data Validation with assertr
Tech talk - Data Validation with assertrAppsilon Data Science
 
Marek Rogala's Talk at User2017 on shiny.collections
Marek Rogala's Talk at User2017 on shiny.collectionsMarek Rogala's Talk at User2017 on shiny.collections
Marek Rogala's Talk at User2017 on shiny.collectionsAppsilon Data Science
 
Scaling Shiny Apps - EARL 2017 San Francisco
Scaling Shiny Apps - EARL 2017 San Francisco Scaling Shiny Apps - EARL 2017 San Francisco
Scaling Shiny Apps - EARL 2017 San Francisco Appsilon Data Science
 

Plus de Appsilon Data Science (10)

User! 2019 best practices for building shiny enterprise applications
User! 2019 best practices for building shiny enterprise applicationsUser! 2019 best practices for building shiny enterprise applications
User! 2019 best practices for building shiny enterprise applications
 
Styling your projects! leveraging css and r sass in r projects
Styling your projects! leveraging css and r sass in r projectsStyling your projects! leveraging css and r sass in r projects
Styling your projects! leveraging css and r sass in r projects
 
SCRUM in Data Science
SCRUM in Data ScienceSCRUM in Data Science
SCRUM in Data Science
 
Successful Machine Learning projects in Fintech
Successful Machine Learning projects in FintechSuccessful Machine Learning projects in Fintech
Successful Machine Learning projects in Fintech
 
Making shiny shine brighter
Making shiny shine brighterMaking shiny shine brighter
Making shiny shine brighter
 
Tech Talk - Overview of Dash framework for building dashboards
Tech Talk - Overview of Dash framework for building dashboardsTech Talk - Overview of Dash framework for building dashboards
Tech Talk - Overview of Dash framework for building dashboards
 
Tech talk - Data Validation with assertr
Tech talk - Data Validation with assertrTech talk - Data Validation with assertr
Tech talk - Data Validation with assertr
 
Marek Rogala's Talk at User2017 on shiny.collections
Marek Rogala's Talk at User2017 on shiny.collectionsMarek Rogala's Talk at User2017 on shiny.collections
Marek Rogala's Talk at User2017 on shiny.collections
 
Scaling Shiny Apps - EARL 2017 San Francisco
Scaling Shiny Apps - EARL 2017 San Francisco Scaling Shiny Apps - EARL 2017 San Francisco
Scaling Shiny Apps - EARL 2017 San Francisco
 
Open Data - Rada Innowacyjności
Open Data - Rada InnowacyjnościOpen Data - Rada Innowacyjności
Open Data - Rada Innowacyjności
 

Dernier

Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyKhushali Kathiriya
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...apidays
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 

Dernier (20)

Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 

Introduction to Generative Adversarial Networks (GANs)

  • 1. Soft introduction to Generative Adversarial Networks (GANs) Appsilon tech talks | Michał Maj | 24/10/2018
  • 2. Agenda Agenda: ● Difference between discriminative and generative algorithms. ● What are GANs ? ● Use cases of GANs. ● Building Vanilla DCGAN in Keras. ● Why it is hard to train a GAN ?
  • 3. Discriminative and generative algorithms Let’s start with a ConvNet for satellite imagery classification (from previous tech talk). As you can remember our task looked like this: we wanted to predict class (ship or non-ship), or to be more specific the probability that the image belongs to the specific class, given the image. Each image was composed of a set of pixels that we were using as features / inputs. Mathematically speaking we were using set of features X (pixels) o get the conditional probability of Y given X: p(y|x) This is an example of discriminative algorithm.
  • 4. Discriminative and generative algorithms Generative algorithms on the other hand are doing something completely opposite. In our example, assuming that the class of an image is ship how should the image look like, so what value should each pixel have? This time we’re generating distribution of X (pixels) given Y(class) or joint probability: p(x|y) or p(x,y)
  • 5. What are GANs ? Generative adversarial networks, introduced in 2014 by Ian Goodfellow, are generative algorithms comprised of two deep neural networks “playing” a zero-sum game against each other. There are many different “flavours” of GANs and depending on the task we will use different networks to build our GAN. For example DCGAN (Deep Convolutional Generative Adversarial Networks) is a GAN designed for basic image generation.
  • 6. What are GANs ? First network is called generator and it’s basically responsible for creating new instances of data from random noise. Second network is called discriminator and it “judges” if the data generated by generator is real or fake having real data to compare.
  • 7. Use cases of GANs ● Data augmentation - e.g. create new instances of data to train your model, alternative to bayesian methods like Gibbs sampling “NVIDIA showed amazing example of this approach in action: they used GANs to augment dataset of medical brain CT images with different diseases and showed that the classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity.”
  • 8. Use cases of GANs ● Image generation - e.g. Disney is generating new textures for animations ● Anomaly detection - GAN learns the predictor distribution so it can detect outliers and anomalies ● Domain adaptation Source: https://www.google.com/url?q=https://medium.com/@alexrachnog/gans-beyond-generation-7-alternative-use-cases-725c60ba95e8?fbclid%3DIwAR2c81U-rmlb3FFWq_s56dafuCV-tvOgjqHtc Ot0KAwm34I8Z3JJHX6XHZk&sa=D&ust=1547052892635000&usg=AFQjCNFWNEG79Y71yW3V9JVMcH3JOhoK2A
  • 9. Use cases of GANs ● Data manipulation Source: https://www.google.com/url?q=https://medium.com/@alexrachnog/gans-beyond-generation-7-alternative-use-cases-725c60ba95e8?fbclid%3DIwAR2c81U-rmlb3FFWq_s56dafuCV-tvOgjqHtcOt0KAwm34I8Z3JJHX6XHZk&sa =D&ust=1547052892635000&usg=AFQjCNFWNEG79Y71yW3V9JVMcH3JOhoK2A https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09
  • 11. Why it is hard to train a GAN ● There’s no fixed minimum of a loss function (it changes in each iteration) ● Discriminator gets too successful (vanishing gradient problem for generator) ● Complicated goal: Source: https://medium.com/@jonathan_hui/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b