1.

Record Nr.

UNINA9910616390203321

Titolo

Deep Generative Models : 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

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022

ISBN

9783031185762

3031185765

Edizione

[1st ed. 2022.]

Descrizione fisica

1 online resource (136 pages)

Collana

Lecture Notes in Computer Science, , 1611-3349 ; ; 13609

Disciplina

006.37

006.31

Soggetti

Computer vision

Machine learning

Education - Data processing

Application software

Computer Vision

Machine Learning

Computers and Education

Computer and Information Systems Applications

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Sommario/riassunto

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.