Things to study

Reading

1. MIT PDF
2. Light Reading
3. Light Reading pt. 2

Google Courses - follow in this order

1. Prerequisites
2. Problem Framing with Machine Learning
3. Machine Learning Crash Course
4. GANs

Keras GAN Library

1. Keras-GAN

Installation

$ git clone https://github.com/eriklindernoren/Keras-GAN
$ cd Keras-GAN/
$ sudo pip3 install -r requirements.txt

GAN tutorials

1. 1D Generative Adversarial Network Demo

2. starter from "How to Train a GAN?" at NIPS2016

3. NIPS 2016 Tutorial: Generative Adversarial Networks

4. OpenAI - Generative Models

random GAN apps

1) Object Detection/Recognition

• Perceptual Generative Adversarial Networks for Small Object Detection, [paper]
• Adversarial Generation of Training Examples for Vehicle License Plate Recognition, [paper]

2) Robotics

• Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks, [paper], [github]

3) Video (generation/prediction)

• DEEP MULTI-SCALE VIDEO PREDICTION BEYOND MEAN SQUARE ERROR, [paper], [github]

4) Synthetic Data Generation

• Learning from Simulated and Unsupervised Images through Adversarial Training, [paper], [github]