| 書目名稱 | Synthetic Data for Deep Learning |
| 編輯 | Sergey I. Nikolenko |
| 視頻video | http://file.papertrans.cn/885/884355/884355.mp4 |
| 概述 | The first book about synthetic data, an important field which is rapidly rising in popularity throughout machine learning.Provides a wide survey of several different fields where synthetic data is or |
| 叢書名稱 | Springer Optimization and Its Applications |
| 圖書封面 |  |
| 描述 | .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 outdoo |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | synthetic data; deep learning; low-level computer vision; object detection; segmentation; GANs; domain tra |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-75178-4 |
| isbn_softcover | 978-3-030-75180-7 |
| isbn_ebook | 978-3-030-75178-4Series ISSN 1931-6828 Series E-ISSN 1931-6836 |
| issn_series | 1931-6828 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
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