You're reading from Hands-On Image Generation with TensorFlow

Product typeBook

Published inDec 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826789

Edition1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Computer Vision

Author (1)

Soon Yau Cheong

Controlling face attributes

Everything we have done in this chapter serves only one purpose: to prepare us for face editing! This is the climax of this chapter!

Latent space arithmetic

We have talked about the latent space several times now but haven't given it a proper definition. Essentially, it means every possible value of the latent variables. In our VAE, it is a vector of 200 dimensions, or simply 200 variables. As much as we hope each variable has a distinctive semantic meaning to us, such as z[0] is for eyes, z[1] dictates the eye color, and so on, things are never that straightforward. We will simply have to assume the information is encoded in all the latent vectors and we can use vector arithmetic to explore the space.

Before diving into high-dimensional space, let's try to understand it using a two-dimensional example. Imagine you are now at point (0,0) on a map and your home is at (x,y). Therefore, the direction toward your home is (x – 0 ,y...

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Hands-On Image Generation with TensorFlow

Published in: Dec 2020Publisher: PacktISBN-13: 9781838826789

Author (1)

Soon Yau Cheong

Soon Yau Cheong is an AI consultant and the founder of Sooner.ai Ltd. With a history of being associated with industry giants such as NVIDIA and Qualcomm, he provides consultation in the various domains of AI, such as deep learning, computer vision, natural language processing, and big data analytics. He was awarded a full scholarship to study for his PhD at the University of Bristol while working as a teaching assistant. He is also a mentor for AI courses with Udacity.
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Controlling face attributes

Latent space arithmetic

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