| 书目名称 | Multi-faceted Deep Learning | | 副标题 | Models and Data | | 编辑 | Jenny Benois-Pineau,Akka Zemmari | | 视频video | | | 概述 | Presents high priority problems in the field of Deep Learning, Multimedia, Visual Data Representation, Interpretation and Coding.Covers low supervision and metric learning.Discusses cross-media and cr | | 图书封面 |  | | 描述 | .This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. .The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. .Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Visio | | 出版日期 | Book 2021 | | 关键词 | Artificial Intelligence; Deep Learning; Deep Neural Networks; low supervision; explainability of Deep l | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-74478-6 | | isbn_softcover | 978-3-030-74480-9 | | isbn_ebook | 978-3-030-74478-6 | | copyright | Springer Nature Switzerland AG 2021 |
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发表于 2025-3-21 18:35:59
