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Generative Adversarial Networks A-Z: State of the art (2019)
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2 hours 24 minutes 
english
Online - Self Paced
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About Generative Adversarial Networks A-Z: State of the art (2019)
Introduction
Generative Learning Motivation
Generative Adversarial Networks
GANs algorithm
Deep Convolutional Generative Adversarial Networks
Measures or Quality
Practice 1d
Practice 2d mode collapse
Practice celeba
Practice celeba 2
Celeba 3
Applications
Cycle GANs
Superresolution
Inpainting
Text2image
Progressive Growing of GANs
Style-based Generator
Game of thrones
BIG GANs
BIG GANs model
BIG GANs techniques
What You Will Learn?
- How to generate high-quality images from noise? Is it really possible?.
- Generative Adversarial Networks were invented in 2014 and since that time it is a breakthrough in the Deep Learning for generation of new objects. Now, in 2019, there exists around a thousand of different types of Generative Adversarial Networks. And it seems impossible to study them all..
- I work with GANs for several years, since 2015. And now I can share with you all my experience, going from the classical algorithm to the advanced techniques and state of the art models. I also added a section with different application of GANs: super-resolution, text to image translation, image to image translation and others..
- This course has rather strong prerequisites:.
- Deep Learning and Machine Learning.
- Matrix Calculus.
- Probability Theory and Statistics.
- Here are tips for taking most from the course:.
- If you don't understand something, ask questions. In case of common questions I will make a new video for everybody..
- Use handwritten notes. Not bookmarks and keyboard typing! Handwritten notes!.
- Don't try to remember all, try to analyse the material..