02978nam 22006735 450 991061639020332120251225202215.09783031185762303118576510.1007/978-3-031-18576-2(MiAaPQ)EBC7107727(Au-PeEL)EBL7107727(CKB)25048788800041(PPN)265855594(BIP)85932564(BIP)85653604(DE-He213)978-3-031-18576-2(EXLCZ)992504878880004120221007d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep 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 Yuan1st ed. 2022.Cham :Springer Nature Switzerland :Imprint: Springer,2022.1 online resource (136 pages)Lecture Notes in Computer Science,1611-3349 ;13609Print version: Mukhopadhyay, Anirban Deep Generative Models Cham : Springer,c2022 9783031185755 Includes bibliographical references and index.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.Lecture Notes in Computer Science,1611-3349 ;13609Computer visionMachine learningEducationData processingApplication softwareComputer VisionMachine LearningComputers and EducationComputer and Information Systems ApplicationsComputer vision.Machine learning.EducationData processing.Application software.Computer Vision.Machine Learning.Computers and Education.Computer and Information Systems Applications.006.37006.31Mukhopadhyay AnirbanMiAaPQMiAaPQMiAaPQBOOK9910616390203321Deep generative models3041715UNINA