Many people these days are fascinated by deep learning, as it enabled new capabilities in many areas, particularly in computer vision. Deep nets are however black boxes and most people have no idea how they work (and frankly most of us, scientists trained in the field can't tell exactly how they work either). But the success of deep learning and a set of its surprising failure modes teach us a valuable lesson about the data we process.
In this post I will present a perspective of what deep learning actually enables, how it relates to classical computer vision (which is far from being dead) and what are the potential dangers of relying on DL for critical applications.
The vision problem
First of all, some things need to be said about the problem of vision/computer vision. In principle it could be formulated as follows: given an image from a camera allow the computer to answer questions about the contents of that image. Such questions can range from "is there a triangle in the image", "is there a human face in the image" to more complex instances such as "is there a dog chasing a cat in the image". Although many of … Read more...