Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13609)
Included in the following conference series:
Conference proceedings info: DGM4MICCAI 2022.
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About this book
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.
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.
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Keywords
Table of contents (12 papers)
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Front Matter
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Methods
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Front Matter
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Applications
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Front Matter
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Back Matter
Other volumes
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Deep Generative Models
Editors and Affiliations
Bibliographic Information
Book Title: Deep Generative Models
Book Subtitle: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Editors: Anirban Mukhopadhyay, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu, Yixuan Yuan
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-031-18576-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-031-18575-5Published: 08 October 2022
eBook ISBN: 978-3-031-18576-2Published: 07 October 2022
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 127
Number of Illustrations: 8 b/w illustrations, 36 illustrations in colour
Topics: Image Processing and Computer Vision, Machine Learning, Computers and Education, Computer Applications
