Generative Adversarial Networks (GANs) are a type of deep learning model used for unsupervised machine learning tasks like image generation. GANs work by having two neural networks, a generator and discriminator, compete against each other. The generator creates synthetic images and the discriminator tries to distinguish real images from fake ones. This allows the generator to improve over time at creating more realistic images that can fool the discriminator. The document discusses the intuition behind GANs, provides a PyTorch implementation example, and describes variants like DCGAN, LSGAN, and semi-supervised GANs.
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.
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.
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.
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
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Introduction to Generative Adversarial Networks (GAN) with Apache MXNetAmazon Web Services
GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.
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
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.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
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.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
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.
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.
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
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Introduction to Generative Adversarial Networks (GAN) with Apache MXNetAmazon Web Services
GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.
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
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.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
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.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Environmental Impact Assessment - University of WinnipegJohn Gunter
On Wednesday, March 11th I conducted a guest lecture at the University of Winnipeg 4th year Science Environmental Impact Assessment class. The course explores the methodology of environmental impact assessment (EIA). Students learn about various types of EIA, the components of EIA review, the regulatory aspects of EIA, and how to complete their own EIA. Students are expected to undertake EIA examples in both written and oral form.
Slides from my talk at Facebook Developer Circle: Pune Launch on Aug 19 2017. Supplementary code available here: https://github.com/mayurbhangale/pytorch_notebook
a general term that can reflect various types of evaluations intended to identify environmental compliance and management system implementation gaps, along with related corrective actions.
In this way they perform an analogous (similar) function to financial audits.
Deep learning an Introduction with Competitive LandscapeShivaji Dutta
This gives an introduction to Neural Networks to CNN, RNN, Reinforcement Learning to what competitive tools are out there. Also a comparison of the various frameworks from Tensorflow, Caffe, Chainer and Pytorch. We also capture the work done by various other companies in the enterprise tools space, web service offerings from Google, Sales Force and Amazon. End we mention the various Heroes of the Deep Learning space.
Summarization of Environmental Impact Assessment Methodology by Dr. I.M. Mis...Arvind Kumar
Summarization of Environmental Impact
Assessment Methodology by Dr. I.M. Mishra
Professor, Dept. of Chemical Engineering
Dean, Saharanpur Campus
Indian Institute of Technology, Roorkee
A study summary on Generative Adversarial Network (GAN)
Alone with Youtube Video in Mandarine (https://www.youtube.com/watch?v=c6mngSqcSIw&feature=youtu.be)
In 4 angles:
GAN Training
GAN vs Latent Space
GAN Autoencoder
Super Resolution GAN
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview
Description
Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
This talk by Lucas Theis from Twitter/Magic Pony on "Compressing Images with Neural Networks" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Slides by Víctor Garcia about the paper:
Reed, Scott, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. "Generative adversarial text to image synthesis." ICML 2016.
Generation of Deepfake images using GAN and Least squares GAN.pptDivyaGugulothu
Our project is to generate deep fake images using deep learning techniques i.e Generative
Adversarial Networks.
We generated Deep Fake images using Traditional GAN and least squares GAN.
EuroSciPy 2019 - GANs: Theory and ApplicationsEmanuele Ghelfi
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).
Variational Autoencoded Regression of Visual Data with Generative Adversarial...NAVER Engineering
발표자: 유영준 (서울대 박사 후 연구원)
발표일: 2017.8.
Postdoctoral Research Associate: Graduate School of Convergence Science and Technology, Seoul National University. March, 2017
See more info at https://sites.google.com/view/yjyoo3312/
개요:
Presents a new visual image generation method using regression combined with recent deep generative models: VAE, GAN.
GDC2019 - SEED - Towards Deep Generative Models in Game DevelopmentElectronic Arts / DICE
Deep learning is becoming ubiquitous in Machine Learning (ML) research, and it's also finding its place in industry-related applications. Specifically, deep generative models have proven incredibly useful at generating and remixing realistic content from scratch, making themselves a very appealing technology in the field of AI-enhanced content authoring. As part of this year's Machine Learning Tutorial at the Game Developers Conference 2019 (GDC), Jorge Del Val from SEED will cover in an accessible manner the fundamentals of deep generative modeling, including some common algorithms and architectures. He will also discuss applications to game development and explore some recent advances in the field.
The attendee will gain basic understanding of the fundamentals of generative models and how to implement them. Also, attendees will grasp potential applications in the field of game development to inspire their work and companies. This talk does not require a mathematical or machine learning background, although previous knowledge on either of those is beneficial.
발표자: 이활석 (Naver Clova)
발표일: 2017.11.
(현) NAVER Clova Vision
(현) TFKR 운영진
개요:
최근 딥러닝 연구는 지도학습에서 비지도학습으로 급격히 무게 중심이 옮겨지고 있습니다.
특히 컴퓨터 비전 기술 분야에서는 지도학습에 해당하는 이미지 내에 존재하는 정보를 찾는 인식 기술에서,
비지도학습에 해당하는 특정 정보를 담는 이미지를 생성하는 기술인 생성 기술로 연구 동향이 바뀌어 가고 있습니다.
본 세미나에서는 생성 기술의 두 축을 담당하고 있는 VAE(variational autoencoder)와 GAN(generative adversarial network) 동작 원리에 대해서 간략히 살펴 보고, 관련된 주요 논문들의 결과를 공유하고자 합니다.
딥러닝에 대한 지식이 없더라도 생성 모델을 학습할 수 있는 두 방법론인 VAE와 GAN의 개념에 대해 이해하고
그 기술 수준을 파악할 수 있도록 강의 내용을 구성하였습니다.
Synthetic Image Data Generation using GAN &Triple GAN.pptxRupeshKumar301638
The presentation focuses on the utilization of different deep generative models for synthetic image generation. These models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Auto-Regressive Models, and Flow-Based Models.
Firstly, the presentation introduces VAEs, which are probabilistic models that aim to encode input images into a latent space and generate new images by sampling from this latent space. It explains the underlying principles of VAEs and their ability to generate diverse and realistic synthetic images.
Next, the presentation delves into GANs, which involve two competing neural networks: a generator network and a discriminator network. The generator network generates synthetic images, while the discriminator network learns to distinguish between real and synthetic images. The presentation describes the training process and the theoretical basis of GANs.
The presentation further explores Auto-Regressive Models, which model the joint probability distribution of the image pixels conditioned on previous pixels. It discusses how these models leverage the dependencies among pixels to generate coherent and high-quality synthetic images.
Flow-Based Models, another class of generative models, are then introduced. These models learn a bijective transformation between a simple base distribution and the target distribution of images. The presentation explains how these models can generate images by sampling from the base distribution and applying the inverse transformation.
Finally, the presentation highlights the Triple GAN, a specific type of GAN that exhibits superiority in synthetic image generation compared to other models and existing GANs. It discusses the unique characteristics of Triple GAN, such as its improved stability and ability to generate high-resolution images. The presentation supports these claims by providing mathematical proofs and presenting implementation results that demonstrate the superior performance of Triple GAN in generating realistic and diverse synthetic images.
Overall, the presentation covers various deep generative models, their principles, and their applications in synthetic image generation. It emphasizes the superiority of Triple GAN, supported by mathematical proofs and implementation results, showcasing its advancements in this field.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
2. 2
Speaker Introduction Introduction
B.S. in Computer Science & Engineering at Korea University
M.S. Student in Computer Science & Engineering at Korea University (Current)
Interest: Deep Learning,TensorFlow, PyTorch
GitHub Link: https://github.com/yunjey
3. 3
Referenced Slides Introduction
• Namju Kim. Generative Adversarial Networks (GAN)
https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091
• Taehoon Kim. 지적 대화를 위한 깊고 넓은 딥러닝
https://www.slideshare.net/carpedm20/ss-63116251
5. 5
Branches of ML Introduction
Semi-supervised
Learning Unsupervised
Learning
Supervised
Learning
Reinforcement
Learning
Machine Learning
No labeled data
No feedback
“find hidden structure”
Labeled data
Direct feedback
No labeled data
Delayed feedback
Reward signal
8. 8
Generative Model
𝑋 1 2 3 4 5 6
𝑃 𝑋
1
6
1
6
1
6
0
6
1
6
2
6
1 2 3 4 5 6
1
6
2
6
𝑝 𝑥
𝑥
Probability Distribution Introduction
Random variable
Probability Basics (Review)
Probability mass function
9. 9
Generative Model
What if 𝑥 is actual images in the training data?
At this point, 𝑥 can be represented as a (for example) 64x64x3 dimensional vector.
Probability Distribution Introduction
11. 11
Generative Model
𝑥
𝑝 𝑑𝑎𝑡𝑎(𝑥)
Probability Distribution Introduction
Let’s take an example with human face image dataset.
Our dataset may contain few images of men with glasses.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥1 is a 64x64x3 high dimensional vector
representing a man with glasses.
The probability
density value is low
12. The probability
density value is high
12
Generative Model
𝑥
𝑝 𝑑𝑎𝑡𝑎(𝑥)
Probability Distribution Introduction
Our dataset may contain many images of women with black hair.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥2 is a 64x64x3 high dimensional vector
representing a woman with black hair.
13. 13
Generative Model
𝑥
𝑝 𝑑𝑎𝑡𝑎(𝑥)
Probability Distribution Introduction
Our dataset may contain very many images of women with blonde hair.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥3 is a 64x64x3 high dimensional vector
representing a woman with blonde hair.
The probability
density value is
very high
14. 14
Generative Model
𝑥
𝑝 𝑑𝑎𝑡𝑎(𝑥)
Probability Distribution Introduction
Our dataset may not contain these strange images.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥4 is an 64x64x3 high dimensional vector
representing very strange images.
The probability
density value is
almost 0
15. 15
Generative Model
𝑥
Probability Distribution Introduction
The goal of the generative model is to find a 𝑝 𝑚𝑜𝑑𝑒𝑙(𝑥) that
approximates 𝑝 𝑑𝑎𝑡𝑎(𝑥) well.
𝑥1 𝑥2 𝑥3 𝑥4
𝑝 𝑚𝑜𝑑𝑒𝑙(𝑥)
𝑝 𝑑𝑎𝑡𝑎(𝑥)
𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑢𝑎𝑙 𝑖𝑚𝑎𝑔𝑒𝑠
𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑖𝑚𝑎𝑔𝑒𝑠 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙
17. 17
Intuition in GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
Fake image
Real image
The probability of that
x came from the real data
(0~1)Discriminator
Generator
Latent Code
May be high
May be low
Training with real images
Training with fake images
18. 18
Intuition in GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
Real image
(64x64x3)
This value should
be close to 1.Discriminator
(Neural Network)
The discriminator should classify
a real image as real.
19. 19
Intuition in GAN GANs
G(z)
Dz D(G(z))
D D(x)
x
Fake image generated by the generator
(64x64x3)
Generator
This value should
be close to 0.
The discriminator should classify
a fake image as fake.
20. 20
Intuition in GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
Generated image
(64x64x3)
Generator
(Neural Network)
Latent Code
(100)
This value should
be close to 1.
The generator should create an image
that is indistinguishable from real to
deceive the discriminator
21. 𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺(𝑧)))
21
Objective Function of GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
𝐺 𝐷
Training with real images
Training with fake images
Maximum when 𝐷(𝑥) = 1 Maximum when 𝐷(𝐺(𝑧)) = 0
Sample 𝑥 from real data distribution Sample latent code 𝑧 from Gaussian distribution
Train D to classify fake images as fake
Train D to classify real images as real
𝐷 should maximize 𝑉(𝐷, 𝐺)
Objective function
22. 𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺(𝑧)))
𝐺 is independent of this part
22
Objective Function of GAN GANs
𝐺 should minimize 𝑉(𝐷, 𝐺) Minimum when 𝐷(𝐺(𝑧)) = 1
G(z)
DGz D(G(z))
D D(x)
x
Train G to deceive D
𝐺 𝐷
Training with real images
Training with fake images
Objective function
29. 29
PyTorch Implementation GANs
G(z) DGz D(G(z))
D D(x)x
D(x) gets closer to 1.
D(G(z)) gets closer to 0
Forward, Backward and Gradient Descent
Train the discriminator
with real images
Train the discriminator
with fake images
30. 30
PyTorch Implementation GANs
G(z) DGz D(G(z))
D D(x)x
Forward, Backward and Gradient Descent
D(G(z)) gets closer to 1
Train the generator
to deceive the discriminator
32. 32
Objective Function of Generator
𝐺
Objective function of G
Non-Saturating Game GANs
Images created by the generator
at the beginning of training
𝑦 = log(1 − 𝑥)
The gradient is relatively small at D(G(z))=0
At the beginning of training, the discriminator can clearly classify the
generated image as fake because the quality of the image is very low.
This means that D(G(z)) is almost zero at early stages of training.
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
33. 33
Solution for Poor Gradient
𝐺
𝑚𝑎𝑥 𝐸𝑧~𝑝 𝑧(𝑧) log 𝐷(𝐺 𝑧 )
𝐺
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
Non-Saturating Game GANs
𝑦 = log(𝑥)
The gradient is very large at D(G(z))=0
Use binary cross entropy loss function with fake label (1)
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧)[−𝑦 log 𝐷(𝐺 𝑧 ) − 1 − 𝑦 log(1 − 𝐷(𝐺 𝑧 )]
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧)[− log 𝐷 𝐺 𝑧 ]
𝐺
𝐺
𝑦 = 1
Modification (heuristically motivated)
• Practical Usage
34. 34
Theory in GAN GANs
𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
𝑚𝑖𝑛 𝐽𝑆𝐷(𝑝 𝑑𝑎𝑡𝑎||𝑝 𝑔)
𝐺, 𝐷
𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺
𝐺 𝐷
𝑥
𝑝 𝑔(𝑥)
𝑝 𝑑𝑎𝑡𝑎(𝑥)
𝐽𝑆𝐷(𝑃||𝑄) =
1
2
𝐾𝐿(𝑃| 𝑀 +
1
2
𝐾𝐿(𝑄| 𝑀
𝑤ℎ𝑒𝑟𝑒 𝑀 =
1
2
(𝑃 + 𝑄) KL Divergence
• Why does GANs work?
same
Please see Appendix for details
Because it actually minimizes the distance between the real data distribution and the model distribution.
Objective function of GANs Jenson-Shannon divergence
36. 36
DCGAN Variants of
GAN
Radford et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
• Deep Convolutional GAN(DCGAN), 2015
The authors present a model that is still highly preferred.
37. 37
DCGAN Variants of
GAN
DGz D(G(z))
D D(x)
x
Use convolution, Leaky ReLU
Use deconvolution, ReLU
• No pooling layer (Instead strided convolution)
• Use batch normalization
• Adam optimizer(lr=0.0002, beta1=0.5, beta2=0.999)
38. 38
DCGAN Variants of
GAN
Radford et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
• Latent vector arithmetic
39. 39
LSGAN Variants of
GAN
Xudong Mao et al. Least Squares Generative Adversarial Networks, 2016
• Least Squares GAN (LSGAN)
Proposed a GAN model that adopts the least squares loss function for the discriminator.
42. 42
LSGAN Variants of
GAN
Vanilla GAN LSGAN
Replace cross entropy loss
to least squares loss (L2)
D(x) gets closer to 1
D(G(z)) gets closer to 0
(same as original)
43. 43
LSGAN Variants of
GAN
Vanilla GAN LSGAN
Replace cross entropy loss
to least squares loss (L2)
D(G(z)) gets closer to 1
(same as original)
44. 44
LSGAN Variants of
GAN
• Results (LSUN dataset)
Xudong Mao et al. Least Squares Generative Adversarial Networks, 2016
46. 46
SGAN Variants of
GAN
D
G D
one-hot vector representing 2
Real image
latent vector z
fake image
(1) FC layer with softmax
• Semi-Supervised GAN
Training with real images
Training with fake images
11 dimension
(10 classes + fake)
(1)
(1)
one-hot vector representing a fake label
one-hot vector representing 5
Augustus Odena et al. Semi-Supervised Learning with Generative Adversarial Netwoks, 2016
47. 47
SGAN Variants of
GAN
• Results (Game Character)
1 2 3 4 5
The generator can create an character image that takes a certain pose.
one-hot vectors
representing class labels
The code will be available soon: https://github.com/yunjey
48. 48
ACGAN Variants of
GAN
Augustus Odena et al. Conditional Image Synthesis with Auxilary Classifier, 2016
• Auxiliary Classifier GAN(ACGAN), 2016
Proposed a new method for improved training of GANs using class labels.
49. 49
ACGAN Variants of
GAN
real or fake?
D
G
real or fake?
D
one-hot vector representing 2
Real image
one-hot vector representing 5
latent vector z
fake image
one-hot vector representing 2
(1) FC layer with sigmoid
(2) FC layer with softmax
(1)
(2)
Discriminator
(multi-task learning)
(1)
(2)
• How does it work?
Training with real images
Training with fake images
51. 51
CycleGAN Extensions
• CycleGAN: Unpaired Image-to-Image Translation
presents a GAN model that transfer an image from a source domain A to a target
domain B in the absence of paired examples.
Jun-Yan Zhu et al. Unpaired Image-to-Image Translation using Cycle Consistent Adversarial Networks, 2017
52. 52
CycleGAN
• How does it work?
real or fake ? DB
GAB
Real Image in domain A Fake Image in domain B
Real Image in domain B
Discriminator for domain B
The generator GAB should generates a horse
from the zebra to deceive the discriminator DB.
Extensions
53. 53
CycleGAN
• How does it work?
DB
GAB
Discriminator for domain B
GBA
Reconstructed Image
L2 Loss
GBA generates a reconstructed image of domain A.
This makes the shape to be maintained
when GAB generates a horse image from the zebra.real or fake ?
Real Image in domain A
Real Image in domain B
Fake Image in domain B
Extensions
56. 56
Text2Image
Scott Reed et al. Generative Adversarial Text to Image Synthesis, 2016
• Generative Adversarial Text to Image Synthesis, 2016
presents a novel model architecture that generates an image from the text.
Extensions
57. 57
Text2Image
D
Sentence Embedding
(100)
Is the image real and
relevant to the sentence?
Concatenate at last conv layer
Discriminator should say ‘yes’.
Real image
(128x128)
• Training with (real image, right text)
Extensions
“A small red bird with a black beak”
58. 58
Text2Image
Fake image
(128x128)
D
G
Z
(100)
Sentence Embedding
(100)
Is the image real and
relevant to the sentence?
right text
Discriminator should say ‘no’.
• Training with (fake image, right text)
Generator should creates an
image relevant to the sentence
to deceive the discriminator.
Extensions
“A small red bird with
a black beak”
59. 59
Text2Image
D Is the image real and
relevant to the sentence?
wrong text
(sampled randomly from the training data)
Discriminator should say ‘no’.
• Training with (real image, wrong text)
Extensions
Real image
(128x128)
“A small yellow bird with a brown beak”
Sentence Embedding
(100)
60. 60
StackGAN
Han Zhang et al. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, 2016
• StackGAN:Text to Photo-realistic Image Synthesis
Extensions
62. 62
StackGAN Extensions
D1 D2
G1 G2
Fake image
(64x64)
Real image
(64x64)
Fake image
(128x128)
Real image
(128x128)
‘real’
or
‘fake’
Generates a 64x64 image
Upscales a 64x64 image to 128x128 (Easier than generating from scratch).
Discriminator for 64x64 image Discriminator for 128x128 image
• Generating 128x128 from 64x64
‘real’
or
‘fake’
Z
(100)
64. 64
Convergence Measure
• Boundary Equilibrium GAN (BEGAN)
Extensions
David Berthelot et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks, 2017
65. 65
Convergence Measure
• Reconstruction Loss
Extensions
Sitao Xiang. On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Network, 2017
Gz
G(z) x
Test images
68. 68
GAN in Supervised Learning
• Machine Translation (Seq2Seq)
Extensions
A B C D
Y
X Y Z<start>
Z <end>X
Should ‘ABCD’ be translated to ‘XYZ’?
Tackling the supervised learning
69. 69
GAN in Supervised Learning
• Machine Translation (GANs)
Extensions
D
G
D
Training with real sentences
Training with fake sentences
A
(English)
B
(Korean)
Does A and B have the same meaning?
The discriminator should say ‘yes’.
Does A and B have the same meaning?
The discriminator should say ‘no’.
A
(English)
B
(Fake Korean)
Lijun Wu et al. Adversarial Neural Machine Translation, 2017
The generator should generate B
that has the same meaning as
A to deceive the discriminator.
