Overview
- The first book about synthetic data, an important field which is rapidly rising in popularity throughout machine learning
- Provides a wide survey of several different fields where synthetic data is or can potentially be useful, including domain adaptation and differential privacy
- Contains a very extensive list of references, and in certain specific fields goes sufficiently in-depth to say that it discusses or at least mentions all relevant work
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 174)
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Table of contents (12 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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 in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field.
In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs.
The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
Authors and Affiliations
About the author
Sergey I. Nikolenko is a computer scientist specializing in machine learning and analysis of algorithms. He is the Head of AI at Synthesis AI, a San Francisco based company specializing on the generation and use of synthetic data for modern machine learning models, and also serves as the Head of the Artificial Intelligence Lab at the Steklov Mathematical Institute at St. Petersburg, Russia. Dr. Nikolenko's interests include synthetic data in machine learning, deep learning models for natural language processing, image manipulation, and computer vision, and algorithms for networking. His previous research includes works on cryptography, theoretical computer science, and algebra.
Bibliographic Information
Book Title: Synthetic Data for Deep Learning
Authors: Sergey I. Nikolenko
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-3-030-75178-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-75177-7Published: 27 June 2021
Softcover ISBN: 978-3-030-75180-7Published: 28 June 2022
eBook ISBN: 978-3-030-75178-4Published: 26 June 2021
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XII, 348
Number of Illustrations: 25 b/w illustrations, 100 illustrations in colour
Topics: Machine Learning, Operations Research, Management Science, Image Processing and Computer Vision