Synthetic data for deep learning / (Record no. 200467688)

MARC details
000 -LEADER
fixed length control field 07800nam a2200565 i 4500
001 - CONTROL NUMBER
control field 200467688
003 - CONTROL NUMBER IDENTIFIER
control field TR-AnTOB
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260327112007.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220330s2021 sz a o 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 3030751783
035 ## - SYSTEM CONTROL NUMBER
System control number (CKB)5590000000516579
System control number (MiAaPQ)EBC6676354
System control number (Au-PeEL)EBL6676354
System control number (OCoLC)1258616314
System control number (PPN)260307181
System control number (BIP)80726784
System control number (BIP)79748467
System control number (iGPub)SPNA0081865
System control number (EXLCZ)995590000000516579
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title İngilizce
050 14 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .N556 2021
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) Q325.5
Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) .N556 2021EBK
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Nikolenko, Sergey I.,
Relator term author.
245 10 - TITLE STATEMENT
Title Synthetic data for deep learning /
Statement of responsibility, etc. Sergey I. Nikolenko.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham, Switzerland :
Name of producer, publisher, distributor, manufacturer Springer,
Date of production, publication, distribution, manufacture, or copyright notice [2021]
Date of production, publication, distribution, manufacture, or copyright notice ©2021
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (354 pages)
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Springer optimization and its applications ;
Volume/sequential designation Volume 174
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Intro -- Preface -- Contents -- Acronyms -- 1 Introduction: The Data Problem -- 1.1 Are Machine Learning Models Hitting a Wall? -- 1.2 One-Shot Learning and Beyond: Less Data for More Classes -- 1.3 Weakly Supervised Training: Trading Labels for Computation -- 1.4 Machine Learning Without Data: Leaving Moore's Law in the Dust -- 1.5 Why Synthetic Data? -- 1.6 The Plan -- 2 Deep Learning and Optimization -- 2.1 The Deep Learning Revolution -- 2.2 A (Very) Brief Introduction to Machine Learning -- 2.3 Introduction to Deep Learning -- 2.4 First-Order Optimization in Deep Learning -- 2.5 Adaptive Gradient Descent Algorithms -- 2.6 Conclusion -- 3 Deep Neural Networks for Computer Vision -- 3.1 Computer Vision and Convolutional Neural Networks -- 3.2 Modern Convolutional Architectures -- 3.3 Case Study: Neural Architectures for Object Detection -- 3.4 Data Augmentations: The First Step to Synthetic Data -- 3.5 Conclusion -- 4 Generative Models in Deep Learning -- 4.1 Introduction to Generative Models -- 4.2 Taxonomy of Generative Models in Deep Learning and Tractable ... -- 4.3 Approximate Explicit Density Models: VAE -- 4.4 Generative Adversarial Networks -- 4.5 Loss Functions in GANs -- 4.6 GAN-Based Architectures -- 4.7 Case Study: GAN-Based Style Transfer -- 4.8 Conclusion -- 5 The Early Days of Synthetic Data -- 5.1 Line Drawings: The First Steps of Computer Vision -- 5.2 Synthetic Data as a Testbed for Quantitative Comparisons -- 5.3 ALVINN: A Self-Driving Neural Network in 1989 -- 5.4 Early Simulation Environments: Robots and the Critique of Simulation -- 5.5 Case Study: MOBOT and The Problems of Simulation -- 5.6 Conclusion -- 6 Synthetic Data for Basic Computer Vision Problems -- 6.1 Introduction -- 6.2 Low-Level Computer Vision -- 6.3 Datasets of Basic Objects -- 6.4 Case Study: Object Detection With Synthetic Data.
Formatted contents note 6.5 Other High-Level Computer Vision Problems -- 6.6 Synthetic People -- 6.7 Other Vision-Related Tasks: OCR and Visual Reasoning -- 6.8 Conclusion -- 7 Synthetic Simulated Environments -- 7.1 Introduction -- 7.2 Urban and Outdoor Environments: Learning to Drive -- 7.3 Datasets and Simulators of Indoor Scenes -- 7.4 Robotic Simulators -- 7.5 Vision-Based Applications in Unmanned Aerial Vehicles -- 7.6 Computer Games as Virtual Environments -- 7.7 Conclusion -- 8 Synthetic Data Outside Computer Vision -- 8.1 Synthetic System Logs for Fraud and Intrusion Detection -- 8.2 Synthetic Data for Neural Programming -- 8.3 Synthetic Data in Bioinformatics -- 8.4 Synthetic Data in Natural Language Processing -- 8.5 Conclusion -- 9 Directions in Synthetic Data Development -- 9.1 Domain Randomization -- 9.2 Improving CGI-Based Generation -- 9.3 Compositing Real Data to Produce Synthetic Datasets -- 9.4 Synthetic Data Produced by Generative Models -- 10 Synthetic-to-Real Domain Adaptation and Refinement -- 10.1 Synthetic-to-Real Domain Adaptation and Refinement -- 10.2 Case Study: GAN-Based Refinement for Gaze Estimation -- 10.3 Refining Synthetic Data with GANs -- 10.4 Making Synthetic Data from Real with GANs -- 10.5 Domain Adaptation at the Feature/Model Level -- 10.6 Domain Adaptation for Control and Robotics -- 10.7 Case Study: GAN-Based Domain Adaptation for Medical Imaging -- 10.8 Conclusion -- 11 Privacy Guarantees in Synthetic Data -- 11.1 Why is Privacy Important? -- 11.2 Introduction to Differential Privacy -- 11.3 Differential Privacy in Deep Learning -- 11.4 Differential Privacy Guarantees for Synthetic Data Generation -- 11.5 Case Study: Synthetic Data in Economics, Healthcare, and Social Sciences -- 11.6 Conclusion -- 12 Promising Directions for Future Work -- 12.1 Procedural Generation of Synthetic Data.
Formatted contents note 12.2 From Domain Randomization to the Generation Feedback Loop -- 12.3 Improving Domain Adaptation with Domain Knowledge -- 12.4 Additional Modalities for Domain Adaptation Architectures -- 12.5 Conclusion -- Appendix References.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
538 ## - SYSTEM DETAILS NOTE
System details note Mode of access: World Wide Web.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on print version record.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
9 (RLIN) 738
655 #7 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books
Source of term local
9 (RLIN) 2032
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
International Standard Book Number 3-030-75177-5
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Springer optimization and its applications ;
Volume/sequential designation Volume 174.
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Springerlink
Uniform Resource Identifier <a href="https://link.springer.com/book/10.1007/978-3-030-75178-4">https://link.springer.com/book/10.1007/978-3-030-75178-4</a>
Public note Online access link to the resource
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type E-Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Inventory number Full call number Barcode Date last seen Cost, replacement price Date shelved Koha item type
    Library of Congress Classification Geçerli değil-e-Kitap / Not applicable-e-Book E-Kitap Koleksiyonu Merkez Kütüphane Merkez Kütüphane 22/07/2025   0.00 BİL Q325.5 .N556 2021EBK EBK03938 22/07/2025 0.00 22/07/2025 E-Book
Devinim Yazılım Eğitim Danışmanlık tarafından Koha'nın orjinal sürümü uyarlanarak geliştirilip kurulmuştur.