You're reading from Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803231334

Length 746 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Authors (2):

V Kishore Ayyadevara

Yeshwanth Reddy

View More author details

Table of Contents (26) Chapters

Preface

1. Section 1: Fundamentals of Deep Learning for Computer Vision

2. Artificial Neural Network Fundamentals FREE CHAPTER

3. PyTorch Fundamentals

4. Building a Deep Neural Network with PyTorch

5. Section 2: Object Classification and Detection

6. Introducing Convolutional Neural Networks

7. Transfer Learning for Image Classification

8. Practical Aspects of Image Classification

9. Basics of Object Detection

10. Advanced Object Detection

11. Image Segmentation

12. Applications of Object Detection and Segmentation

13. Section 3: Image Manipulation

14. Autoencoders and Image Manipulation

15. Image Generation Using GANs

16. Advanced GANs to Manipulate Images

17. Section 4: Combining Computer Vision with Other Techniques

18. Combining Computer Vision and Reinforcement Learning

19. Combining Computer Vision and NLP Techniques

20. Foundation Models in Computer Vision

21. Applications of Stable Diffusion

22. Moving a Model to Production

23. Other Books You May Enjoy

24. Index

Appendix

Understanding deepfakes

We have learned about two different image-to-image tasks so far: semantic segmentation with UNet and image reconstruction with autoencoders. Deepfakery is an image-to-image task that has a very similar underlying theory.

How deepfakes work

Imagine a scenario where you want to create an application that takes an image of a face and changes the expression in a way that you want. Deepfakes come in handy in this scenario. While we made a conscious choice to not discuss the very latest in deepfakes in this book, techniques such as few-shot adversarial learning are developed to generate realistic images with the facial expression of interest. Knowledge of how deepfakes work and GANs (which you will learn about in the next chapters) will help you identify videos that are fake.

In the task of deepfakery, we have a few hundred pictures of person A and a few hundred pictures of person B (or, possibly a video of people A and B). The objective is to reconstruct...