Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Dajiang Zhu

ISBN 10: 3030882098 / ISBN 13: 9783030882099

Editore: Springer Nature Switzerland AG, CH, 2021

Riguardo questo articolo

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.

Codice articolo LU-9783030882099

Dati bibliografici

• Titolo: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
• Editore: Springer Nature Switzerland AG, CH
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030882098
• ISBN 13: 9783030882099
• Rilegatura: Paperback
• Edizione: 1st ed. 2021.
• Condizione: New
• Dimensioni: 15,5 L x 1,7 A x 23,5 P centimetri 15,5 centimetri di larghezza per 1,7 centimetri di altezza per 23,5 centimetri di profondità
• Peso: 456 grammi

Descrizione articolo

Riassunto

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 rigorous study of medical data related to machine learning systems.