Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks, a generator and discriminator, compete against each other. The generator learns to generate new data with the same statistics as the training set to fool the discriminator, while the discriminator learns to better distinguish real samples from generated samples. GANs have applications in image generation, image translation between domains, and image completion. Training GANs can be challenging due to issues like mode collapse.
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.
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
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.
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.
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.
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
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
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.
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
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.
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.
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.
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
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
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
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
ICASSP 2018 Tutorial: Generative Adversarial Network and its Applications to ...宏毅 李
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
The tutorial includes three parts. The first part provides a thorough review of GAN. We will first introduce GAN to newcomers. Then, we will introduce the approaches that aim to improve the training procedure and the variants of GAN beyond simply generating random objects. The second part of this tutorial will focus on the applications of GAN on speech. Although most techniques related to GAN are developed on image generation today, GAN can also generate speech. However, speech signals are temporal sequences which have very different nature from images. We will describe how to apply GAN on speech signal processing, including text-to-speech synthesis, voice conversion, speech enhancement, and domain adversarial training on speech-related tasks. The third part of this tutorial will focus on the applications of GAN on natural language processing. The major challenge for applying GAN on natural language is its discrete nature (words are usually represented by one-hot encodings), which makes the original GAN fails. We will review a series of approaches dealing with this problem, and finally demonstrate the applications of GAN on chat-bot, abstractive summarization, and text style transformation.
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.
Abstract: Generative models, and in particular adversarial ones, are becoming prevalent in computer vision as they enable enhancing artistic creation, inspire designers, prove usefulness in semi-supervised learning or robotics applications.
We will see how to develop the abilities of Generative Adversarial Networks (GANs) to
deviate from training examples to generate more original images of fashion designs. As a limitation of GANs is the production of raw images of low resolution, we also present solutions to produce vectorized results, and show how the developed method may be useful for image editing.
Image generation. Gaussian models for human faces, limits and relations with linear neural networks. Generative adversarial networks (GANs), generators, discrinators, adversarial loss and two player games. Convolutional GAN and image arithmetic. Super-resolution. Nearest-neighbor, bilinear and bicubic interpolation. Image sharpening. Linear inverse problems, Tikhonov and Total-Variation regularization. Super-Resolution CNN, VDSR, Fast SRCNN, SRGAN, perceptual, adversarial and content losses. Style transfer: Gatys model, content loss and style loss.
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.
These are the slides from the London Creative AI meetup on "Generating audio and images using deep learning" given by Kai Arulkumaran on 23rd November.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
4. Learn how to manipulate features
Discriminative Generative
Learn about a decision boundary between classes
Figure Source : https://datawarrior.wordpress.com/2016/05/08/generative-discriminative-pairs/
classify, discriminate, differentiate, label generate new samples: new media, new music , new text
Naïve Bayes , Hidden Markov models , Gaussian mixtures , Boltzman
Machines , Variational Autoencoders , Generative Adversarial Networks
Traditional Neural Networks , SVMs , Logistic
regression ,Nearest neighbors , Random Forests
7. Make me a movie with..
Make me a video with..
Make me a picture with..
Make me a soundtrack with..
Make me a room with..
Make me a video game with..
% 100 machine generated life
What is Exciting !
Image : techcrunch.com/Magic Pony
Image : MIT Tech Review
https://www.wired.com/story/ai-filmmaker-zone-out/
8. What does Generative Models do?
generate a model distribution which close to true distribution
model distribution target distribution target
model
target
model
Earlier attempt Latest attempt
pdf pdf
10. What if a deep network used to judge the
generation process and give feedback on
generated images
Novel Idea : Adversarial Training
Since deep networks are very successful to recognize
images like cat and dog,
Generation Process
External Judger
13. Z : Random Noise
R : Real samples
G : Generator
D Discriminator
Basic Framework
Z
R
Switch Train
Source : https://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html
16. D(x)
x
More on the Framework
Probability distribution of x
Discriminator
D(G(z))
G(z)
z
Probability
distribution
(logits) of G of z
Generated fake image
Generator Discriminator
17. D(G(z))
G(z)
z
More on the Framework
Probability
distribution
(logits) of G of z
Generated fake image
Generator Discriminator
D(x)
Probability distribution of x
Discriminator
18. Logits : Dx , Labels :1
Logits : Dg , Labels : 0
D_loss_real (Dx , ones) D_loss_fake (Dg , zeros)
X : real images
Z
(Random Noise)
generator
discriminator
D(x) tries to be near 1
LossES
D_loss =
Discriminator training
Generator training
D tries to make D(G(z)) near 0,
Gz : fake images
19. Logits : Dx , Labels :1
Logits : Dg , Labels : 0
D_loss_real (Dx , ones) D_loss_fake (Dg , zeros)
Gz : fake images
X : real images
Z
(noise vector)
generator
discriminator
D(x) tries to be near 1
LossES
G tries to make D(G(z)) near 1
D_loss =
G_loss = G_loss (Dg , ones)
Logits : Dg , Labels : 1
Discriminator training
Generator training
D tries to make D(G(z)) near 0,
Gz : fake images
21. Discriminator
Generator
Step 1. Train Discriminator
Backpropagation
Step 2. Train Generator
Discriminator
Generator Backpropagation
only classifier mode
Freeze generator
Z
Z
Only generate samples Update parameters
Generate samples
Freeze discriminator
Update parameters
22. Challenges !
Mode collapse
If one of them fails, the whole system fails
discriminator much powerful
generator too lenient
discriminator too lenient
Generator much powerful generating similar samples
Generator explores some weakness in discriminator
Top : desired case
Bottom : collapsed case
If else
Metz , 2016
print
31. Source Paper : https://arxiv.org/abs/1612.03242
Text to Photo-realistic Image Synthesis (StackGAN)
Given a text description of a bird, generate a new bird image to match the text.
32. Source Paper : https://arxiv.org/pdf/1607.07539.pdf
Image completion
38. [1] Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville,
Yoshua Bengio
https://arxiv.org/abs/1406.2661
[2] Understanding Generative Adversarial Networks
Nooki Shibuya
https://github.com/udacity/deep-learning/tree/master/gan_mnist
[3] Generative Adversarial Networks Youtube Series
Siraj Raval
https://www.youtube.com/watch?v=0VPQHbMvGzg&t=1752s
[4] How to Train a GAN? Tips and tricks to make GANs work
Facebook AI Research: Soumith Chintala, Emily Denton, Martin Arjovsky, Michael Mathieu
https://github.com/soumith/ganhacks
[5] Generative Models
Agustinus Kristiadi
https://github.com/wiseodd/generative-models
[6] Generative Models
Analytics Vidhya , Introductory guide to Generative Adversarial Networks (GANs)
39. [7] Image Completion with Deep Learning in TensorFlow
Brandon Amos
http://bamos.github.io/2016/08/09/deep-completion/
[8] GAN MNIST Example in TensorFlow
Udacity
https://github.com/udacity/deep-learning/tree/master/gan_mnist
[9] Generative Adversarial Networks
Robin Richard
http://www.rricard.me/machine/learning/generative/adversarial/networks/keras/tensorflow/2017/04/05/gans-
part1.html
[10] GAN Tutorial Series
MLNotebook
https://mlnotebook.github.io/post/GAN1/
[11] GAN Tutorial
DL4J
https://deeplearning4j.org/generative-adversarial-network
[12] Generative Adversarial Networks
Yunjey Choi
https://www.slideshare.net/YunjeyChoi/generative-adversarial-networks-75916964
40. [13] Generative Adversarial Networks
JaeJun Yoo
https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo
[14] Variational Autoencoders
Ali Ghodsi
https://www.youtube.com/watch?v=uaaqyVS9-rM&t=574s
[15] Generative Models
Fei Fei Li , Justin Johnson, Serena Yeung
https://www.youtube.com/watch?v=5WoItGTWV54
[16] Machine Learning is Fun Part 7:GANs
Adam Geitgey
https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7
[17] Generative Adversarial Networks
Lara Hulbert
https://medium.com/@lehulbert/generative-adversarial-networks-85da3ec78fd2
[18] Upscaling images CSI-style with generative adversarial neural networks
Geoffrey Litt
http://geoffreylitt.com/2017/06/04/enhance-upscaling-images-with-generative-adversarial-neural-networks.html
