Understanding Implicit Neural Representations with Itzik Ben-Shabat

In this episode of Computer Vision Decoded, we are going to dive into implicit neural representations. We are joined by Itzik Ben-Shabat, a Visiting Research Fellow at the Australian National Universit (ANU) and Technion – Israel Institute of Technology as well as the host of the Talking Paper Podcast. You will learn a core understanding of implicit neural representations, key concepts and terminology, how it's being used in applications today, and Itzik's research into improving output with limit input data. Episode timeline: 00:00 Intro 01:23 Overview of what implicit neural representations are 04:08 How INR compares and contrasts with a NeRF 08:17 Why did Itzik pursued this line of research 10:56 What is normalization and what are normals 13:13 Past research people should read to learn about the basics of INR 16:10 What is an implicit representation (without the neural network) 24:27 What is DiGS and what problem with INR does it solve? 35:54 What is OG-I NR and what problem with INR does it solve? 40:43 What software can researchers use to understand INR? 49:15 What information should non-scientists be focused to learn about INR? Itzik's Website: https://www.itzikbs.com/ Follow Itzik on Twitter: https://twitter.com/sitzikbs Follow Itzik on LinkedIn: https://www.linkedin.com/in/yizhak-itzik-ben-shabat-67b3b1b7/ Talking Papers Podcast: https://talking.papers.podcast.itzikbs.com/ Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85 Referenced past episode- What is CVPR: https://share.transistor.fm/s/15edb19d This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io