Multi-Faceted Deep Learning: Models and Data
暫譯: 多面向深度學習：模型與數據
Benois-Pineau, Jenny, Zemmari, Akka
- 出版商: Springer
- 出版日期: 2021-10-20
- 售價: $7,920
- 貴賓價: 9.5 折 $7,524
- 語言: 英文
- 頁數: 322
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030744779
- ISBN-13: 9783030744779
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
商品描述
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 Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
商品描述(中文翻譯)
本書涵蓋了人工智慧領域中一系列方法，特別是深度學習應用於現實世界問題的技術。書中首先總結了深度學習方法的基本原理以及不同類型的深度神經網絡（Deep Neural Networks, DNNs），為後續以問題為導向的章節提供了全面的前言。
本書討論了深度學習框架下機器學習中最有趣且未解決的問題，並提出了解決方案。書中說明了如何使用深度神經網絡分類器實現零樣本學習（zero-shot learning），這需要大量的訓練數據。缺乏標註的訓練數據自然促使研究人員實施低監督算法。度量學習（Metric learning）是一項長期的研究，但在深度學習方法的框架下，它獲得了新鮮感和創新性。低類別間變異性的細粒度分類（fine-grained classification）對於任何分類任務來說都是一個困難的問題。本書展示了如何通過在三維卷積網絡中使用不同的模態和注意力機制來解決這一問題。
專注於機器學習、深度學習、多媒體和計算機視覺的研究人員將會想要購買本書。學習這些主題領域的高級計算機科學學生也會發現本書非常有用。
作者簡介
Prof. Jenny Benois-Pineau is a full professor of Computer Science at the University Bordeaux. Her topics of interest include image/multimedia, artificial intelligence in multimedia and healthcare. She is the author and co-author of more than 200 papers in international journals, conference proceedings, books and book chapters. She is associated editor of Eurasip SPIC, ACM MTAP, senior associated editor JEI SPIE journals. She has organized workshops and special sessions at international conferences IEEE ICIP, ACM MM, ... She has served in numerous program committees in international conferences: ACM MM, ACM ICMR, ACM CIVR, CBMI, IPTA, ACM MMM. She has been coordinator or leading researcher in EU - funded and French national research projects. She is a member of IEEE TC IVMSP. She has Knight of Academic Palms grade.
Dr. Akka Zemmari has received his Ph.D. degree from the University of Bordeaux 1, France, in 2000. He is an associate professor in computer science since 2001 at University of Bordeaux, France. His research interests include Artificial Intelligence, Deep Learning, Distributed algorithms and systems, Graphs, Randomized Algorithms, and Security. He wrote one book and more than 80 research papers published in international journals and conference proceedings and he is involved in program committees and organization committees of international conferences.
作者簡介(中文翻譯)
珍妮·貝諾瓦斯-皮諾教授是波爾多大學的計算機科學全職教授。她的研究興趣包括影像/多媒體、人工智慧在多媒體和醫療保健中的應用。她是超過200篇國際期刊、會議論文、書籍及書籍章節的作者和合著者。她是Eurasip SPIC、ACM MTAP的副編輯，以及JEI SPIE期刊的高級副編輯。她曾在國際會議如IEEE ICIP、ACM MM等組織工作坊和特別會議。她在多個國際會議的程序委員會中擔任過職務，包括ACM MM、ACM ICMR、ACM CIVR、CBMI、IPTA、ACM MMM。她曾擔任歐盟資助和法國國家研究項目的協調員或首席研究員。她是IEEE TC IVMSP的成員，並獲得學術棕櫚騎士勳章。
阿卡·澤瑪里博士於2000年在法國波爾多大學獲得博士學位。自2001年以來，他在法國波爾多大學擔任計算機科學副教授。他的研究興趣包括人工智慧、深度學習、分散式演算法和系統、圖形、隨機演算法以及安全性。他撰寫了一本書和超過80篇發表於國際期刊和會議論文的研究論文，並參與國際會議的程序委員會和組織委員會。
