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001437719 020__ $$a9783030751784$$q(electronic bk.)
001437719 020__ $$a3030751783$$q(electronic bk.)
001437719 020__ $$z9783030751777
001437719 020__ $$z3030751775
001437719 0247_ $$a10.1007/978-3-030-75178-4$$2doi
001437719 035__ $$aSP(OCoLC)1258616314
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001437719 049__ $$aISEA
001437719 050_4 $$aQ325.5$$b.N55 2021
001437719 08204 $$a006.3/1$$223
001437719 1001_ $$aNikolenko, Sergey I.,$$eauthor.
001437719 24510 $$aSynthetic data for deep learning /$$cSergey I. Nikolenko.
001437719 264_1 $$aCham :$$bSpringer,$$c[2021]
001437719 264_4 $$c©2021
001437719 300__ $$a1 online resource (xii, 348 pages) :$$billustrations (chiefly color)
001437719 336__ $$atext$$btxt$$2rdacontent
001437719 337__ $$acomputer$$bc$$2rdamedia
001437719 338__ $$aonline resource$$bcr$$2rdacarrier
001437719 347__ $$atext file
001437719 347__ $$bPDF
001437719 4901_ $$aSpringer optimization and its applications,$$x1931-6828 ;$$vvolume 174
001437719 504__ $$aIncludes bibliographical references.
001437719 5050_ $$a1. 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.
001437719 506__ $$aAccess limited to authorized users.
001437719 520__ $$aThis 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.
001437719 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 2, 2021).
001437719 650_0 $$aMachine learning.
001437719 650_0 $$aComputer vision.
001437719 650_6 $$aApprentissage automatique.
001437719 650_6 $$aVision par ordinateur.
001437719 655_0 $$aElectronic books.
001437719 77608 $$iPrint version:$$z3030751775$$z9783030751777$$w(OCoLC)1245656763
001437719 830_0 $$aSpringer optimization and its applications ;$$vv. 174.$$x1931-6828
001437719 852__ $$bebk
001437719 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-75178-4$$zOnline Access$$91397441.1
001437719 909CO $$ooai:library.usi.edu:1437719$$pGLOBAL_SET
001437719 980__ $$aBIB
001437719 980__ $$aEBOOK
001437719 982__ $$aEbook
001437719 983__ $$aOnline
001437719 994__ $$a92$$bISE