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Multi-faceted deep learningmodels an...

Benois-Pineau, Jenny.

Multi-faceted deep learningmodels and data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-faceted deep learningedited 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.
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$q(electronic bk.)

Multi-faceted deep learningmodels 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$q(electronic bk.)

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

188639
Machine learning.

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

Dewey Class. No.: 006.31

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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.
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