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Modern Computer Vision with PyTorch

You're reading from  Modern Computer Vision with PyTorch

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Pages 824 pages
Edition 1st Edition
Languages
Authors (2):
V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Profile icon Yeshwanth Reddy
View More author details

Table of Contents (25) Chapters

Preface Section 1 - Fundamentals of Deep Learning for Computer Vision
Artificial Neural Network Fundamentals PyTorch Fundamentals Building a Deep Neural Network with PyTorch Section 2 - Object Classification and Detection
Introducing Convolutional Neural Networks Transfer Learning for Image Classification Practical Aspects of Image Classification Basics of Object Detection Advanced Object Detection Image Segmentation Applications of Object Detection and Segmentation Section 3 - Image Manipulation
Autoencoders and Image Manipulation Image Generation Using GANs Advanced GANs to Manipulate Images Section 4 - Combining Computer Vision with Other Techniques
Training with Minimal Data Points Combining Computer Vision and NLP Techniques Combining Computer Vision and Reinforcement Learning Moving a Model to Production Using OpenCV Utilities for Image Analysis Other Books You May Enjoy Appendix

Performing neural style transfer

In neural style transfer, we have a content image and a style image, and we combine these two images in such a way that the combined image preserves the content of the content image while maintaining the style of the style image.

An example style image and content image are as follows:

In the preceding picture, we want to retain the content in the picture on right (the content image), but overlay it with the color and texture in the picture on the left (the style image).

The process of performing neural style transfer is as follows. We try to modify the original image in a way that the loss value is split into content loss and style loss. Content loss refers to how different the generated image is from the content image. Style loss refers to how correlated the style image is to the generated image.

While we mentioned that the loss is calculated based on the difference in images, in practice, we modify it slightly by ensuring that the loss is calculated...

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