You're reading from Generative Adversarial Networks Projects

Product typeBook

Published inJan 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789136678

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Deep Learning

Author (1)

Kailash Ahirwar

Summary

In this chapter, we have learned about and implemented a StackGAN network to generate high-resolution images from text descriptions. We started with a basic introduction to StackGAN, in which we explored the architectural details of a StackGAN and discovered the losses used for the training of StackGAN. Then, we downloaded and prepared the dataset. After that, we started implementing the StackGAN in the Keras framework. After the implementation, we trained the Stage-I and Stage-II StackGANS sequentially. After successfully training the network, we evaluated the model and saved it for further use.

In the next chapter, we will work with CycleGAN, a network that can convert paintings into photos.

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Generative Adversarial Networks Projects

Published in: Jan 2019Publisher: PacktISBN-13: 9781789136678

Author (1)

Kailash Ahirwar

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
Read more about Kailash Ahirwar

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Author (1)

Hands-On Generative Adversarial Networks with PyTorch 1.x

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