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008			210626s2021 gw | o |||| 0|eng d
020			_a9783030751784
024	7		_a10.1007/978-3-030-75178-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100			_aNikolenko, Sergey _9681246 _cSynthetic Data for Deep Learning
245	1	0	_aSynthetic Data for Deep Learning _cby Sergey I. Nikolenko.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XII, 348 páginas) _b125 ilustraciones, 100 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringer Optimization and Its Applications _x1931-6828 _v174
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_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.
520	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783030751777
776	0	8	_iPrinted edition: _z9783030751791
776	0	8	_iPrinted edition: _z9783030751807
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-75178-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eh _zSI