Publicado por Springer Nature Switzerland AG, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Publicado por Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Añadir al carritoPaperback. Condición: New. 1st ed. 2021. This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems.
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Publicado por Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Añadir al carritoCondición: New. This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in Oct.
Publicado por Springer International Publishing, Springer Nature Switzerland Sep 2021, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.
Publicado por Springer International Publishing, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, andpromising future directions in Deep Generative Models. Deep generative modelssuch as Generative Adversarial Network (GAN) and Variational Auto-Encoder(VAE) are currently receiving widespread attention from not only the computervision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems.
Publicado por Springer Nature Switzerland, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Añadir al carritoTaschenbuch. Condición: Neu. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections | First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings | Sandy Engelhardt (u. a.) | Taschenbuch | xv | Englisch | 2021 | Springer Nature Switzerland | EAN 9783030882099 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Publicado por Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
Idioma: Inglés
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Añadir al carritoPaperback. Condición: New. 1st ed. 2021. This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems.
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Añadir al carritoPaperback. Condición: Brand New. 293 pages. 9.25x6.10x9.25 inches. In Stock. This item is printed on demand.
Publicado por Springer International Publishing Sep 2021, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
Idioma: Inglés
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, andpromising future directions in Deep Generative Models. Deep generative modelssuch as Generative Adversarial Network (GAN) and Variational Auto-Encoder(VAE) are currently receiving widespread attention from not only the computervision and machine learning communities, but also in the MIC and CAI community.For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. 296 pp. Englisch.
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