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Hands-On Image Generation with TensorFlow

You're reading from Hands-On Image Generation with TensorFlow A practical guide to generating images and videos using deep learning

Product type Paperback

Published in Dec 2020

Publisher Packt

ISBN-13 9781838826789

Length 306 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Computer Vision

Author (1):

Soon Yau Cheong

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Fundamentals of Image Generation with TensorFlow

2. Chapter 1: Getting Started with Image Generation Using TensorFlow FREE CHAPTER

3. Chapter 2: Variational Autoencoder

4. Chapter 3: Generative Adversarial Network

5. Section 2: Applications of Deep Generative Models

6. Chapter 4: Image-to-Image Translation

7. Chapter 5: Style Transfer

8. Chapter 6: AI Painter

9. Section 3: Advanced Deep Generative Techniques

10. Chapter 7: High Fidelity Face Generation

11. Chapter 8: Self-Attention for Image Generation

12. Chapter 9: Video Synthesis

13. Chapter 10: Road Ahead

14. Other Books You May Enjoy

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Generating faces with VAEs

Now that you have learned the theory of VAEs and have built one for MNIST, it is time to grow up, ditch the toy, and generate some serious stuff. We will use VAE to generate some faces. Let's get started! The code is in ch2_vae_faces.ipynb. There are a few face datasets available for training:

Celeb A (http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). This is a popular dataset in academia as it contains annotations of face attributes, but unfortunately it is not available for commercial use.
Flickr-Faces-HQ Dataset (FFHQ) (https://github.com/NVlabs/ffhq-dataset). This dataset is freely available for commercial use and contains high-resolution images.

In this exercise, we will only assume the dataset contains RGB images; feel free to use any dataset that suits your needs.

Network architecture

We reuse the MNIST VAE and training pipeline with some modifications given that the dataset is now different from MNIST. Feel free to reduce...

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Authors (1)

Soon Yau Cheong

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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