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008			211019s2021 sz | s |||| 0|eng d
020			_a9783030744786
024	7		_a10.1007/978-3-030-74478-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
245	1	0	_aMulti-faceted Deep Learning _bModels and Data _cedited by Jenny Benois-Pineau, Akka Zemmari.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XII, 316 páginas) _b86 ilustraciones, 66 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_a1. 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	3		_aThis 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.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aBenois-Pineau, Jenny _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aZemmari, Akka _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030744779
776	0	8	_iPrinted edition: _z9783030744793
776	0	8	_iPrinted edition: _z9783030744809
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-74478-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eh _zSI