A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
A basic introduction to Generative Adversarial Networks, what they are, how they work, and why study them. This presentation shows what is their contribution to Machine Learning field and for which reason they have been considered one of the major breakthroughts in Machine Learning field.
A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)Thomas da Silva Paula
A basic introduction to Generative Adversarial Networks, what they are, how they work, and why study them. This presentation shows what is their contribution to Machine Learning field and for which reason they have been considered one of the major breakthroughts in Machine Learning field.
basics of GAN neural network
GAN is a advanced tech in area of neural networks which will help to generate new data . This new data will be developed based over the past experiences and raw data.
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
Slides by Víctor Garcia about:
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation Using Conditional Adversarial Networks".
In arxiv, 2016.
https://phillipi.github.io/pix2pix/
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
In these slides, Generative Adversarial Network (GAN) is briefly introduced, and some GAN applications in medical imaging are presented. In the conclusions, some comments are given for persons who are interested in research of medical imaging using GAN.
A presentation about the development of the ideas from the autoencoder to the Stable Diffusion text-to-image model.
Models covered: autoencoder, VAE, VQ-VAE, VQ-GAN, latent diffusion, and stable diffusion.
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples.
GANS
Ian Goodfellow, OpenAI Research Scientist
Generative Adversarial Networks and Their ApplicationsArtifacia
This is the presentation from our AI Meet Jan 2017 on GANs and its applications.
You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks.
The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers.
We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.
basics of GAN neural network
GAN is a advanced tech in area of neural networks which will help to generate new data . This new data will be developed based over the past experiences and raw data.
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
Slides by Víctor Garcia about:
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation Using Conditional Adversarial Networks".
In arxiv, 2016.
https://phillipi.github.io/pix2pix/
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
In these slides, Generative Adversarial Network (GAN) is briefly introduced, and some GAN applications in medical imaging are presented. In the conclusions, some comments are given for persons who are interested in research of medical imaging using GAN.
A presentation about the development of the ideas from the autoencoder to the Stable Diffusion text-to-image model.
Models covered: autoencoder, VAE, VQ-VAE, VQ-GAN, latent diffusion, and stable diffusion.
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAIWithTheBest
This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples.
GANS
Ian Goodfellow, OpenAI Research Scientist
Generative Adversarial Networks and Their ApplicationsArtifacia
This is the presentation from our AI Meet Jan 2017 on GANs and its applications.
You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Generative Adversarial Networks is an advanced topic and requires a prior basic understanding of CNNs. Here is some pre-reading material for you.
- https://arxiv.org/pdf/1406.2661v1.pdf
- https://arxiv.org/pdf/1701.00160v1.pdf
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks.
The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers.
We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.
The power of deep learning models applicationsSameera Sk
I have performed a detailed analysis of recent advances in deep-learning based research efforts applied in the domains of Image Processing, Audio Processing and Video Processing. I have identified 25 relevant papers and 17 web resources, examining the particular area and problem they address, models employed, datasets used and the images from respective papers are reproduced with proper citation.
i hope this information is useful who are doing research in this area.
Deep Learning Applications and Image Processingijtsrd
With the rapid development of digital technologies, the analysis and processing of data has become an important problem. In particular, classification, clustering and processing of complex and multi structured data required the development of new algorithms. In this process, Deep Learning solutions for solving Big Data problems are emerging. Deep Learning can be described as an advanced variant of artificial neural networks. Deep Learning algorithms are commonly used in healthcare, facial and voice recognition, defense, security and autonomous vehicles. Image processing is one of the most common applications of Deep Learning. Deep Learning software is commonly used to capture and process images by removing the errors. Image processing methods are used in many fields such as medicine, radiology, military industry, face recognition, security systems, transportation, astronomy and photography. In this study, current Deep Learning algorithms are investigated and their relationship with commonly used software in the field of image processing is determined. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "Deep Learning Applications and Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49142.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49142/deep-learning-applications-and-image-processing/ahmet-özcan
An Extensive Review on Generative Adversarial Networks GAN’sijtsrd
This paper is to provide a high level understanding of Generative Adversarial Networks. This paper will be covering the working of GAN’s by explaining the background idea of the framework, types of GAN’s in the industry, it’s advantages and disadvantages, history of how GAN’s are developed and enhanced along the timeline and some applications where GAN’s outperforms themselves. Atharva Chitnavis | Yogeshchandra Puranik "An Extensive Review on Generative Adversarial Networks (GAN’s)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42357.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42357/an-extensive-review-on-generative-adversarial-networks-gan’s/atharva-chitnavis
Alessandro Ferrari - Smart City, Mixed Reality, Self-Driving Cars & Neural Co...Codemotion
The study of the human brain is thousands of years old. With the advent of modern electronics, it was natural to try to harness the thinking process. The first step toward artificial neural networks came in 1943 when McCulloch and Pitts defined how neurons might work. They modeled a simple neural network with electrical circuits. Here the proto-AI age started. 70 years later we entered in the second neural age: self-driving cars, mixed-reality, virtual reality and photo-realistic neural contents. Are you ready for the AI r-Evolution?
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to use several class specific generators, which limits its application to datasets with a limited number of classes. Instead, we suggest a new Group Decreasing Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting semantic labels to natural images and can produce plausible high quality results for datasets with a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in the SMIS mission. We also demonstrate that GroupDNet can perform a variety of interesting synthesis tasks. Ashish Jobson | Dr. Kamalraj R "Fashion AI Literature" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42378.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42378/fashion-ai-literature/ashish-jobson
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
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/
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
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
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/
Editor's Notes
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 ...
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
