000 | 04394nam a22005535i 4500 | ||
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001 | 978-3-030-75178-4 | ||
003 | DE-He213 | ||
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007 | cr nn 008mamaa | ||
008 | 210626s2021 sz | s |||| 0|eng d | ||
020 |
_a9783030751784 _9978-3-030-75178-4 |
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024 | 7 |
_a10.1007/978-3-030-75178-4 _2doi |
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_a006.31 _223 |
100 | 1 |
_aNikolenko, Sergey I. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aSynthetic Data for Deep Learning _h[electronic resource] / _cby Sergey I. Nikolenko. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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300 |
_aXII, 348 p. 125 illus., 100 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringer Optimization and Its Applications, _x1931-6836 ; _v174 |
|
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 | _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. | ||
650 | 0 | _aMachine learning. | |
650 | 0 | _aOperations research. | |
650 | 0 | _aManagement science. | |
650 | 0 | _aComputer vision. | |
650 | 1 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aOperations Research, Management Science. |
650 | 2 | 4 | _aComputer Vision. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030751777 |
776 | 0 | 8 |
_iPrinted edition: _z9783030751791 |
776 | 0 | 8 |
_iPrinted edition: _z9783030751807 |
830 | 0 |
_aSpringer Optimization and Its Applications, _x1931-6836 ; _v174 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-75178-4 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c178179 _d178179 |