Deep Generative Models [electronic resource] : Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Anirban Mukhopadhyay, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu, Yixuan Yuan.
Contributor(s): Mukhopadhyay, Anirban [editor.]
| Oksuz, Ilkay [editor.] | Engelhardt, Sandy [editor.] | Zhu, Dajiang [editor.] | Yuan, Yixuan [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 13609Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: X, 127 p. 44 illus., 36 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031185762.Subject(s): Computer vision | Machine learning | Education -- Data processing | Application software | Computer Vision | Machine Learning | Computers and Education | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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.
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