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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

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838826789
Length 306 pages
Edition 1st Edition
Languages
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Author (1):
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Soon Yau Cheong Soon Yau Cheong
Author Profile Icon Soon Yau Cheong
Soon Yau Cheong
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Table of Contents (15) Chapters Close

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

Video synthesis overview

Let's say your doorbell rings while you're watching a video, so you pause the video and go to answer the door. What would you see on your screen when you come back? A still picture where everything is frozen and not moving. If you press the play button and pause it again quickly, you will see another image that looks very similar to the previous one but with slight differences. Yes – when you play a series of images sequentially, you get a video.

We say that image data has three dimensions, or (H, W, C); video data has four dimensions, (T, H, W, C), where T is the temporal (time) dimension. It's also the case that video is just a big batch of images, except that we cannot shuffle the batch. There must be temporal consistency between the images; I'll explain this further.

Let's say we extract images from some video datasets and train an unconditional GAN to generate images from random noise input. As you can imagine, the...

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