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Python Image Processing Cookbook

You're reading from  Python Image Processing Cookbook

Product type Book
Published in Apr 2020
Publisher Packt
ISBN-13 9781789537147
Pages 438 pages
Edition 1st Edition
Languages
Author (1):
Sandipan Dey Sandipan Dey
Profile icon Sandipan Dey
Toc

Table of Contents (11) Chapters close

Preface 1. Image Manipulation and Transformation 2. Image Enhancement 3. Image Restoration 4. Binary Image Processing 5. Image Registration 6. Image Segmentation 7. Image Classification 8. Object Detection in Images 9. Face Recognition, Image Captioning, and More 10. Other Books You May Enjoy

Using a variational autoencoder to reconstruct and generate images

A variational autoencoder (VAE) is a generative model that uses Bayesian inference and tries to model the underlying probability distribution of images so that it can sample new images from that distribution. Just like an ordinary autoencoder, it's composed of two components: an encoder (a bunch of layers that will compress the input to the bottleneck in a vanilla autoencoder) and a decoder (a bunch of layers that will reconstruct the input from its compressed representation from the bottleneck in a vanilla autoencoder). The difference between a VAE and an ordinary autoencoder is that instead of mapping an input layer to a latent variable, known as the bottleneck vector, the encoder maps the input to a distribution. The random samples are then drawn from the distribution and fed to the decoder.

Since it cannot...

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