Generative Adversarial Networks (GANs)

•Download as PPTX, PDF•

0 likes•92 views

Generative Adversarial Networks (GANs) are a class of machine learning frameworks where two neural networks contest with each other in a game. A generator network generates new data instances, while a discriminator network evaluates them for authenticity, classifying them as real or generated. This adversarial process allows the generator to improve over time and generate highly realistic samples. The document discusses GAN architectures like DCGAN and cGAN, applications for image generation, translation, and deepfakes. It also covers challenges like mode collapse and vanishing gradients.

Software

Iranian Society of Machine Vision and Image Processing (ISMVIP)
Intro to Generative
Adversarial Networks (GANs)
Pegah Salehi
2020 - 17 - Dec
Email: pghsalehi@gmail.com

Generative Adversarial Networks (GANs)
[https://www.scopus.com/]18-1
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
2020
[https://thispersondoesnotexist.com/]
Iranian Society of Machine Vision and Image Processing (ISMVIP)

The GAN framework
Real Data
D
G
Fake Sample
JD
JG
𝐷(𝑥)
Real
or
FakeRandom Noise
18-2
[1]
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

The GAN framework
(1)
(2)
(3)
X 𝑑𝑎𝑡𝑎
D
Gz 𝐺(𝑧)
JD
JG
𝐷(𝑥)
Real
or
Fake
18-3
[1]
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Training process
18-4
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Challenges
• Non-convergence: the model parameters oscillate,
destabilize and never converge
• Mode collapse: the generator collapses which
produces limited varieties of samples
• Diminished gradient: the discriminator gets too
successful that the generator gradient vanishes and
learns nothing
18-5
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Generate New Sample of Image Dataset
DCGA
N
BigGA
N
StyleGA
N
Characteristic
animation
18-6
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

semantic-image-to-photo translation
http://nvidia-research-mingyuliu.com/gaugan/
GauGA
N
Pix2Pix_H
D
18-7
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

https://www.youtube.com/watch?v=cQ54GDm1eL0&feature=youtu.be&t=32
Deepfake videos
18-8
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Text-to-Image Translation
stack
GAN
18-9
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Types of GAN Models
Architectural
Optimization
Objective Function
Optimization
Autoencoder
Conditional
Convolutional
DCGAN
CGAN, infoGAN,
ACGAN, SGAN
AAE, BiGAN, ALI,
AGE, VAE-GAN
Unrolled GAN, f-GAN, Mode-Regularized
GAN, Least-Square GAN, EBGAN,
WGAN, WGAN-GP, WGAN-LP
GenerativeAdversarialNetwork
(GAN)
18-10
[1]
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

32×32×128
4×4×1024
z
Decov 1
Decov 2
Decov 3
Decov 4
8×8×512
16×16×256
64×64×3
G(z)
100 Output
DCGAN
18-11
[1]
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

X
D
Gz G z c
Real
or
Fake
C (class)
cGAN
18-12
• Goal: Better control of the generation
• Idea: Add information about the generated sample (e.g., labels) to train the generator
[1]
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Image-to-Image Translation
Pair Unpair
18-13
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Pix2Pix
Fake Pair
Real
or
Fake
Input
Ground Truth (Reference)
U-net
PatchGAN
Discriminator
Generator
Generated
Tune G by computing L 1 distance
between output and ground truth.
Compute
Adversarial
Loss
Tune G
Tune D
Real Pair
18-14
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

[towardsdatascience.com]
cycleGAN
18-15
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Introducing Education
Resources
18-16
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

References
1. Salehi, Pegah, Abdolah Chalechale, and Maryam
Taghizadeh. "Generative Adversarial Networks (GANs):
An Overview of Theoretical Model, Evaluation Metrics,
and Recent Developments." arXiv preprint
arXiv:2005.13178 (2020).
18-17
Architecture
s
Applications
Main
Concept
Reference
Challenges
Introductio
n
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Thank you
for your attention...
18-18
Iranian Society of Machine Vision and Image Processing (ISMVIP)

Similar to Generative Adversarial Networks (GANs)

lec19.pdf

ssuser0d87ff

Computer Graphics Practical

Neha Sharma

LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)

Hansol Kang

CAR DAMAGE DETECTION USING DEEP LEARNING

IRJET Journal

Tracxn - Top Business Models - China Tech - Apr 2022

Tracxn

TEXT TO IMAGE GENERATION USING GAN

IRJET Journal

IRJET- Generating 3D Models Using 3D Generative Adversarial Network

IRJET Journal

EuroSciPy 2019: https://pretalx.com/euroscipy-2019/talk/Q79NND/ GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production. The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues. We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0. The workshop is composed of: - Theoretical introduction - GANs from Scratch in TensorFlow 2.0 - High-performance input data pipeline with TensorFlow Datasets - Introduction to the AshPy API - Implementing, training, and visualizing DCGAN using AshPy - Serving TF2 Models with Google Cloud Functions The materials of the workshop will be openly provided via GitHub (https://github.com/zurutech/gans-from-theory-to-production).

EuroSciPy 2019 - GANs: Theory and Applications

Emanuele Ghelfi

[DLHacks 実装]Perceptual Adversarial Networks for Image-to-Image Transformation

Deep Learning JP

Tackling Open Images Challenge (2019)

Hiroto Honda

Tracxn - Top Business Models - AR VR - Industry Applications - Apr 2022

Tracxn

발표자: 박태성 (UC Berkeley 박사과정) 발표일: 2017.6. Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros. His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University. 개요: Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.

Finding connections among images using CycleGAN

NAVER Engineering

An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...

IRJET Journal

Image super resolution using Generative Adversarial Network.

IRJET Journal

Tracxn - Top Business Models - AR VR Industry Applications Tech - Feb 2022

Tracxn

IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...

IRJET Journal

Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”

Lviv Startup Club

In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.

IMAGE GENERATION WITH GANS-BASED TECHNIQUES: A SURVEY

ijcsit

Image Generation with Gans-based Techniques: A Survey

AIRCC Publishing Corporation

GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production. Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine. Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production Github repo: https://github.com/zurutech/gans-from-theory-to-production

GAN - Theory and Applications

Emanuele Ghelfi

Similar to Generative Adversarial Networks (GANs) (20)

lec19.pdf

Computer Graphics Practical

LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)

CAR DAMAGE DETECTION USING DEEP LEARNING

Tracxn - Top Business Models - China Tech - Apr 2022

TEXT TO IMAGE GENERATION USING GAN

IRJET- Generating 3D Models Using 3D Generative Adversarial Network

EuroSciPy 2019 - GANs: Theory and Applications

[DLHacks 実装]Perceptual Adversarial Networks for Image-to-Image Transformation

Tackling Open Images Challenge (2019)

Tracxn - Top Business Models - AR VR - Industry Applications - Apr 2022

Finding connections among images using CycleGAN

An Intelligent approach to Pic to Cartoon Conversion using White-box-cartooni...

Image super resolution using Generative Adversarial Network.

Tracxn - Top Business Models - AR VR Industry Applications Tech - Feb 2022

IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...

Vladislav Kolbasin “Introduction to Generative Adversarial Networks (GANs)”

IMAGE GENERATION WITH GANS-BASED TECHNIQUES: A SURVEY

Image Generation with Gans-based Techniques: A Survey

GAN - Theory and Applications

Recently uploaded

(Vivek)Call Us, 8448380779,Call girls in Delhi NCr – We Offer best in class call girls. escort Service At Affordable Price At low Rate with Space Night 8000 We Are One Of The Oldest Escort and Call girls Agencies in Delhi. You Will Find That Our Female Escorts Are Full Of Fun, Sexy And They Would Love Enjoy Your Company. We Have A Fantastic Selection Of Escort Ladies Available For In-Calls As Well As Out-Calls. Our Escorts Are Not Only Beautiful But All Have Great Personalities Making Them The Perfect Companion For Any Occasion. In-Call:- You Can Come At Our Place in Delhi Our place Which Is Very Clean Hygienic 100% safe Accommodation. Out-Call:- You have To Come Pick The Girl From My Place We Are Also Provide Door Step Services (Delhi Ncr, Noida, Gurgaon, Faridabad, Ghaziabad Note:- Pic Collectors Time Passers Bargainers Stay Away As We Respect The Value For Your Money Time And Expect The Same From You Hygienic:- Full Ac room And Clean Rooms Available In Hotel 24 * 7 Hourly In Delhi NCR More Details, With WhatsApp Number, +91-8448380779

Sector 18, Noida Call girls :8448380779 Model Escorts | 100% verified

Delhi Call girls

In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Alberto González Trastoy

A Secure and Reliable Document Management System is Essential.docx

ComplianceQuest1

HR Software Buyers Guide in 2024 - HRSoftware.com

Fatema Valibhai

Software Quality Assurance Interview Questions

Arshad QA

10 Trends Likely to Shape Enterprise Technology in 2024

Mind IT Systems

LEVEL 5 - SESSION 1 2023 (1).pptx - PDF 123456

KiaraTiradoMicha

The title is not connected to what is inside

shinachiaurasa2

A great deal of attention in medical devices has shifted towards cybersecurity with the ratification of section 524B of the FD&C act. This new law enables the FDA to enforce cybersecurity controls in any medical device that is capable of networked communications or that has software. In this webinar we will recap the process for managing vulnerabilities, identify categories of vulnerabilities and solutions and more.

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

ICS

Crypto Cloud Review - How To Earn Up To $500 Per DAY Of Bitcoin 100% On AutoP...

SelfMade bd

MarTech Trend 2024 Book : Marketing Technology Trends (2024 Edition) How Data...

Jittipong Loespradit

%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein

masabamasaba

Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid

Philip Schwarz

+971565801893 Mtp-Kit (500MG) Prices » Dubai [(+971565801893**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Leen Whatsapp +971565801893 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971565801893''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971565801893' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Clinic in Abu Dhabi, United Arab Emirates.+971565801893

+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...

Health

At the recent Microsoft Ignite 2023 conference, Microsoft unveiled a groundbreaking strategy that will redefine enterprise work management. The plan involves integrating Microsoft’s key planning tools, Microsoft To Do, Microsoft Planner, and Microsoft Project for the web into a unified experience called “Microsoft Planner.” What does this new strategy from Microsoft mean for current users? Join us and learn how best to take advantage of this announcement while gaining a clear path on how to elevate the current state of Microsoft Planner from a basic task manager to a comprehensive tool for Enterprise Work Management using OnePlan. Learn how OnePlan’s integration with Microsoft Planner allows for strategic alignment with business goals through advanced features like strategic planning, portfolio management, resource management, financial management, and more!

Introducing Microsoft’s new Enterprise Work Management (EWM) Solution

OnePlan Solutions

%in ivory park+277-882-255-28 abortion pills for sale in ivory park

masabamasaba

MakeMyPass" Online Bus Pass Management System illustrates the flow of activities and actions that occur within the system to accomplish specific tasks or use cases. This type of diagram focuses on representing the sequence of activities and decision points involved in a particular process. Below is an example outline and description of key elements that could be included in an Activity Diagram for the system:

BUS PASS MANGEMENT SYSTEM USING PHP.pptx

alwaysnagaraju26

In an era where security concerns are paramount, the integration of artificial intelligence (AI) into CCTV cameras has revolutionized surveillance capabilities. One of the most significant advancements is the ability to achieve real-time threat detection, enabling immediate responses to potential security breaches. This blog explores how AI is reshaping surveillance through real-time threat detection and the implications of this technology.

Optimizing AI for immediate response in Smart CCTV

shikhaohhpro

In the past six months, the AI landscape has undergone a massive transformation, ushering in a new era of productivity with the latest in Large Language Models (LLMs) and AI technology. This deep dive unlocks how to: Create CustomGPT Models: No coding needed to tailor AI for your unique projects. Integrate your own data, including PDFs and Excel sheets, making information handling a breeze. Plus, discover how to call your own actions/integrations for even more personalized utility. Navigate Advanced Prompting: Overcome AI's memory limits and utilize Retrieval-Augmented Generation for accessing your personalized data, streamlining how you interact with AI. Stay Ahead with AI Trends: Peek into the evolving world of LLMs, featuring newcomers like Google Gemini, Anthropic Claude, Open Sora, and Twitter Grok, and understand what their advancements mean for your productivity. Witness Real-Life Transformations: Through examples and prompt demonstrations, see firsthand how these AI strategies revolutionize routine tasks, from data analysis to content creation. Learn to leverage image output and input for advanced practical use cases, adding a new dimension to your productivity toolkit. No previous coding or AI experience is needed for this talk. Stay ahead in the fast-evolving world of work. Embrace the AI revolution and transform your workflow with advanced LLM techniques. Join us to ensure you're not left behind in the productivity race.

AI Mastery 201: Elevating Your Workflow with Advanced LLM Techniques

VictorSzoltysek

ManageIQ - Sprint 236 Review - Slide Deck

ManageIQ

Recently uploaded (20)