Synthetic Data For Deep Learning By Sergey I Nikolenko
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Synthetic Data for Deep Learning
Author: Sergey I. Nikolenko
language: en
Publisher: Springer Nature
Release Date: 2021-06-26
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.
Data Protection and Privacy, Volume 16
Author: Hideyuki Matsumi
language: en
Publisher: Bloomsbury Publishing
Release Date: 2024-05-02
This book explores the complexity and depths of our digital world by providing a selection of analyses and discussions from the 16th annual international conference on Computers, Privacy and Data Protection (CPDP): Ideas that Drive Our Digital World. The first half of the book focuses on issues related to the GDPR and data. These chapters provide a critical analysis of the 5-year history of the complex GDPR enforcement system, covering: codes of conduct as a potential co-regulation instrument for the market; an interdisciplinary approach to privacy assessment on synthetic data; the ethical implications of secondary use of publicly available personal data; and automating technologies and GDPR compliance. The second half of the book shifts focus to novel issues and ideas that drive our digital world. The chapters offer analyses on social and environmental sustainability of smart cities; reconstructing states as information platforms; stakeholder identification using the example of video-based Active and Assisted Living (AAL); and a human-centred approach to dark patterns. This interdisciplinary book takes readers on an intellectual journey into a wide range of issues and cutting-edge ideas to tackle our ever-evolving digital landscape.
Handbook on the Politics and Governance of Big Data and Artificial Intelligence
Author: Andrej Zwitter
language: en
Publisher: Edward Elgar Publishing
Release Date: 2023-06-01
Drawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance. This title contains one or more Open Access chapters.