Computer Vision News - November 2022

5 Xingjian Zhen problem is not yet fully solved, and they hope this work opens a path for people in academia and beyond to dig deeper. “ There are many applications we don’t believe can be done in academia, ” Rudra continues. “ Theseare thingsonlyacompany or industry can do, like self-driving, for example. They train huge networks, and we think our method could reduce their training and computational time to make themmore effective. I hope this opens more research in that direction. ” Xingjian adds: “ As a university researcher, it’s more like you’re on the demo side. We show things work, and then to make it profitable or applicable at a business level, “ We all know as vision and machine learning scientists that not all theory holds in practice, even if it’s promising, ” Rudra points out. “ The challenging part is how to make it applicable in practice. Then comes the complexity, such as memory usage, how much GPU we have, and all those things. ” Understanding networks is a hot topic for researchers. While this work demonstrates how effective something as simple as correlation can be to help, the team is keen to point out that this is just one way to compare networks, and there are many other statistical tools that people could use, some simple and some more complex, that have not currently been explored in computer vision or machine learning. The T R V