東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multi-faceted deep learning = models...

Benois-Pineau, Jenny.

FindBook

Google Book

Amazon

博客來

Multi-faceted deep learning = models and data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-faceted deep learning/ edited by Jenny Benois-Pineau, Akka Zemmari.
其他題名:	models and data /
其他作者:	Benois-Pineau, Jenny.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xii, 316 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Deep Neural Networks: Models and methods -- 3. Deep learning for semantic segmentation -- 4. Beyond Full Supervision in Deep Learning -- 5. Similarity Metric Learning -- 6. Zero-shot Learning with Deep Neural Networks for Object Recognition -- 7. Image and Video Captioning using Deep Architectures -- 8. Deep Learning in Video Compression Algorithms -- 9. 3D Convolutional Networks for Action Recognition: Application toSport Gesture Recognition -- 10. Deep Learning for Audio and Music -- 11. Explainable AI for Medical Imaging:Knowledge Matters -- 12. Improving Video Quality with Generative Adversarial Networks -- 13. Conclusion.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-74478-6
ISBN:	9783030744786

Multi-faceted deep learning = models and data /
Multi-faceted deep learningmodels and data /[electronic resource] :edited by Jenny Benois-Pineau, Akka Zemmari. - Cham :Springer International Publishing :2021. - xii, 316 p. :ill., digital ;24 cm.

1. Introduction -- 2. Deep Neural Networks: Models and methods -- 3. Deep learning for semantic segmentation -- 4. Beyond Full Supervision in Deep Learning -- 5. Similarity Metric Learning -- 6. Zero-shot Learning with Deep Neural Networks for Object Recognition -- 7. Image and Video Captioning using Deep Architectures -- 8. Deep Learning in Video Compression Algorithms -- 9. 3D Convolutional Networks for Action Recognition: Application toSport Gesture Recognition -- 10. Deep Learning for Audio and Music -- 11. Explainable AI for Medical Imaging:Knowledge Matters -- 12. Improving Video Quality with Generative Adversarial Networks -- 13. Conclusion.

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.

ISBN: 9783030744786

Standard No.: 10.1007/978-3-030-74478-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M85 2021

Dewey Class. No.: 006.31

Multi-faceted deep learning = models and data /
LDR:02973nmm a2200325 a 4500 001 2253473
003 DE-He213
005 20211019234234.0
006 m d
007 cr nn 008maaau
008 220327s2021 sz s 0 eng d
020 $a 9783030744786 $q (electronic bk.)
020 $a 9783030744779 $q (paper)
024 7 $a 10.1007/978-3-030-74478-6 $2 doi
035 $a 978-3-030-74478-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M85 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M961 2021
245 0 0 $a Multi-faceted deep learning $h [electronic resource] : $b models and data / $c edited by Jenny Benois-Pineau, Akka Zemmari.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xii, 316 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. Deep Neural Networks: Models and methods -- 3. Deep learning for semantic segmentation -- 4. Beyond Full Supervision in Deep Learning -- 5. Similarity Metric Learning -- 6. Zero-shot Learning with Deep Neural Networks for Object Recognition -- 7. Image and Video Captioning using Deep Architectures -- 8. Deep Learning in Video Compression Algorithms -- 9. 3D Convolutional Networks for Action Recognition: Application toSport Gesture Recognition -- 10. Deep Learning for Audio and Music -- 11. Explainable AI for Medical Imaging:Knowledge Matters -- 12. Improving Video Quality with Generative Adversarial Networks -- 13. Conclusion.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Multimedia Information Systems. $3 892521
650 2 4 $a Image Processing and Computer Vision. $3 891070
700 1 $a Benois-Pineau, Jenny. $3 2157624
700 1 $a Zemmari, Akka. $3 3382614
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-74478-6
950 $a Computer Science (SpringerNature-11645)