  This is an opinion paper about the strengths and weaknesses of Deep Nets for
vision. They are at the heart of the enormous recent progress in artificial
intelligence and are of growing importance in cognitive science and
neuroscience. They have had many successes but also have several limitations
and there is limited understanding of their inner workings. At present Deep
Nets perform very well on specific visual tasks with benchmark datasets but
they are much less general purpose, flexible, and adaptive than the human
visual system. We argue that Deep Nets in their current form are unlikely to be
able to overcome the fundamental problem of computer vision, namely how to deal
with the combinatorial explosion, caused by the enormous complexity of natural
images, and obtain the rich understanding of visual scenes that the human
visual achieves. We argue that this combinatorial explosion takes us into a
regime where "big data is not enough" and where we need to rethink our methods
for benchmarking performance and evaluating vision algorithms. We stress that,
as vision algorithms are increasingly used in real world applications, that
performance evaluation is not merely an academic exercise but has important
consequences in the real world. It is impractical to review the entire Deep Net
literature so we restrict ourselves to a limited range of topics and references
which are intended as entry points into the literature. The views expressed in
this paper are our own and do not necessarily represent those of anybody else
in the computer vision community.
