Synthetic Data for Deep Learning

Idioma: inglés

Editorial: Springer, Springer Nature Switzerland Jun 2021, 2021

3030751775 / 9783030751777

Serie: Libro 166 de 176 - Springer Optimization and Its Applications

Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 360 pp. Englisch.…

N° de ref. del artículo 9783030751777

Título
Synthetic Data for Deep Learning
Autor
Sergey I. Nikolenko
Editorial
Springer, Springer Nature Switzerland Jun 2021
Año de publicación
2021
Estado
Neu
Encuadernación
Buch
Idioma
inglés
ISBN 10
3030751775
ISBN 13
9783030751777
Peso del artículo
705 gramos
Dimensiones
241x160x25 mm
Serie
Libro 166 de 176: Springer Optimization and Its Applications

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Emtmannsberg, BAYE, Alemania

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