Have We Forgotten about Geometry in Computer Vision?
<i>However, these models are largely big black-boxes. There are a lot of things we don’t understand about them.</i><p>I&#x27;m getting kind of sick of this &quot;deep learning is a black box&quot; trope, because it&#x27;s really not true anymore. Yes, it&#x27;s a black box if you just use &quot;some data and 20 lines of code using a basic deep learning API&quot; as mentioned in the article. But if you spend some time understanding the architecture of networks and read the latest literature, it&#x27;s pretty clear how they function, how they encode data, and how and why they learn what they do.<p>Because neural networks are so dramatically more effective than they used to be, in so many domains, it&#x27;s true that we don&#x27;t yet have a good understanding of optimal ways to build, train, and optimize networks. But that is <i>exactly</i> why there is so much excitement -- because there is a lot to discover, and a lot of progress that can be made quickly.<p>I agree with the author that fundamental physical and geometric approaches are still relevant and useful, and have been somewhat ignored recently, but the fact remains: If you and I as individuals want to maximize our personal impact, and capture as much value as we can while working on interesting problems, deep learning is an <i>excellent</i> field in which to do that.<p>It&#x27;s kind of like we just discovered a nice vein of gold, and the silver miners are like &quot;yeah, but we don&#x27;t know much about that vein of gold and how long it will last.&quot; Which is true, but in the meantime, there&#x27;s a lot of easy money to be made, and ultimately, both types of resources are important and synergistic.