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

Name: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
Brand: Springer Nature Switzerland AG
SKU: 9783030882099
Price: 24894 HUF
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Szerző: Anirban Mukhopadhyay, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Yuan Xue, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman

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Kötés: Puha kötésű

Kiadó: Springer Nature Switzerland AG

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This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Model...

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Szerző

Anirban Mukhopadhyay, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Yuan Xue, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2021

oldal

278

EAN

9783030882099

ISBN

3030882098

Enbook ID

37225308

Kiadó

Springer Nature Switzerland AG

Súly

456

Méretek

155 x 235 x 17

Teljes leírás

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.

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

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