Synthetic Data for Deep Learning /

This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon...

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Bibliographic Details
Main Author:	Nikolenko, Sergey I. (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Series:	Springer Optimization and Its Applications, 174
Subjects:	Machine learning. Operations research. Management science. Optical data processing. Machine Learning. Operations Research, Management Science. Image Processing and Computer Vision. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

1. Introduction
2. Synthetic data for basic computer vision problems
3. Synthetic simulated environments
4. Synthetic data outside computer vision
5. Directions in synthetic data development
6. Synthetic-to-real domain adaptation and refinement
7. Privacy guarantees in synthetic data
8. Promising directions for future work
Conclusion
References.