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Synthetic Data for Deep Learning
Taschenbuch von Sergey I. Nikolenko
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

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.

Zusammenfassung

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

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 360
Reihe: Springer Optimization and Its Applications
Inhalt: xii
348 S.
25 s/w Illustr.
100 farbige Illustr.
348 p. 125 illus.
100 illus. in color.
ISBN-13: 9783030751807
ISBN-10: 3030751805
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Nikolenko, Sergey I.
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Springer International Publishing AG
Springer Optimization and Its Applications
Maße: 235 x 155 x 20 mm
Von/Mit: Sergey I. Nikolenko
Erscheinungsdatum: 28.06.2022
Gewicht: 0,546 kg
preigu-id: 121976188
Über den Autor

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.

Zusammenfassung

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

Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 360
Reihe: Springer Optimization and Its Applications
Inhalt: xii
348 S.
25 s/w Illustr.
100 farbige Illustr.
348 p. 125 illus.
100 illus. in color.
ISBN-13: 9783030751807
ISBN-10: 3030751805
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Nikolenko, Sergey I.
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Springer International Publishing AG
Springer Optimization and Its Applications
Maße: 235 x 155 x 20 mm
Von/Mit: Sergey I. Nikolenko
Erscheinungsdatum: 28.06.2022
Gewicht: 0,546 kg
preigu-id: 121976188
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