You're reading from Advanced Deep Learning with Python

Product typeBook

Published inDec 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789956177

Edition1st Edition

Languages

Python

Tools

PyTorch TensorFlow

Concepts

Deep Learning

Author (1)

Ivan Vasilev

Introduction to GANs

In this section, we'll talk about arguably the most popular generative model today: the GAN framework. It was first introduced in 2014 in the landmark paper Generative Adversarial Nets (http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf). The GAN framework can work with any type of data, but its most popular application by far is to generate images, and we'll discuss them in this context only. Let's see how it works:

A GAN system

A GAN is a system of two components (neural networks):

Generator: This is the generative model itself. It takes a probability distribution (random noise) as input and tries to generate a realistic output image. Its purpose is similar to the decoder part of the VAE.
Discriminator: This takes two alternating inputs: real images of the training dataset or generated fake samples from the generator. It tries...

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Advanced Deep Learning with Python

Published in: Dec 2019Publisher: PacktISBN-13: 9781789956177

Author (1)

Ivan Vasilev

Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, with whom he continued its development. He has also worked as a machine learning engineer and researcher in medical image classification and segmentation with deep neural networks. Since 2017, he has focused on financial machine learning. He co-founded an algorithmic trading company, where he's the lead engineer. He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic.
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