Synthetic data for deep learning / Sergey I. Nikolenko.
2021
Q325.5 .N55 2021
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Details
Title
Synthetic data for deep learning / Sergey I. Nikolenko.
ISBN
9783030751784 (electronic bk.)
3030751783 (electronic bk.)
9783030751777
3030751775
3030751783 (electronic bk.)
9783030751777
3030751775
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource (xii, 348 pages) : illustrations (chiefly color)
Other Standard Identifiers
10.1007/978-3-030-75178-4 doi
Call Number
Q325.5 .N55 2021
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 2, 2021).
Series
Springer optimization and its applications ; v. 174. 1931-6828
Available in Other Form
Print version: 9783030751777
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Online Access
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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.
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.