Deep Generative Models : Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

Anirban Mukhopadhyay

ISBN 10: 3031185757 / ISBN 13: 9783031185755

Published by Springer Nature Switzerland, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore.DG4MICCAI 2022 accepted 12 papers from the 15 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.

Seller Inventory # 9783031185755

Bibliographic Details

• Title: Deep Generative Models : Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
• Publisher: Springer Nature Switzerland
• Publication Date: 2022
• Language: English
• ISBN 10: 3031185757
• ISBN 13: 9783031185755
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 8D millimeters 235 millimeters width by 155 millimeters height by 8 millimeters depth
• Weight: 224 grams

About this title

Synopsis

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. 

DG4MICCAI 2022 accepted 12 papers from the 15 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.