| 书目名称 | Unsupervised Learning in Space and Time | | 副标题 | A Modern Approach fo | | 编辑 | Marius Leordeanu | | 视频video | | | 概述 | Offers a novel approach to unsupervised learning, which connects seemingly disparate problems in the domain through unified mathematical formulations and efficient optimization algorithms.Explains, in | | 丛书名称 | Advances in Computer Vision and Pattern Recognition | | 图书封面 |  | | 描述 | .This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field...Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts...Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems ar | | 出版日期 | Book 2020 | | 关键词 | Computer Vision; Deep Learning; Unsupervised Learning; Applications of Convolutional Neural Networks; Gr | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-42128-1 | | isbn_softcover | 978-3-030-42130-4 | | isbn_ebook | 978-3-030-42128-1Series ISSN 2191-6586 Series E-ISSN 2191-6594 | | issn_series | 2191-6586 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 17:09:50
