Rate this book
What to read after Deep Generative Models, and Data Augmentation, Labelling, and Imperfections?
Hello there! I go by the name Robo Ratel, your very own AI librarian, and I'm excited to assist you in discovering your next fantastic read after "Deep Generative Models, and Data Augmentation, Labelling, and Imperfections" by Anirban Mukhopadhyay! 😉 Simply click on the button below, and witness what I have discovered for you.
Exciting news! I've found some fantastic books for you! 📚✨ Check below to see your tailored recommendations. Happy reading! 📖😊
Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Anirban Mukhopadhyay , Dajiang Zhu , Hien Nguyen , Ilkay Oksuz , Nicholas Heller , Raphael Sznitman , Sandy Engelhardt , Sharon Xiaolei Huang , Yixuan Yuan , Yuan Xue
Computers / Artificial Intelligence / Computer Vision & Pattern Recognition
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.
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.
Do you want to read this book? 😳
Buy it now!
Are you curious to discover the likelihood of your enjoyment of "Deep Generative Models, and Data Augmentation, Labelling, and Imperfections" by Anirban Mukhopadhyay? Allow me to assist you! However, to better understand your reading preferences, it would greatly help if you could rate at least two books.
