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This is an opinion paper about the strengths and weaknesses of Deep Nets forvision. They are at the heart of the enormous recent progress in artificialintelligence and are of growing importance in cognitive science andneuroscience. They have had many successes but also have several limitationsand there is limited understanding of their inner workings. At present DeepNets perform very well on specific visual tasks with benchmark datasets butthey are much less general purpose, flexible, and adaptive than the humanvisual system. We argue that Deep Nets in their current form are unlikely to beable to overcome the fundamental problem of computer vision, namely how to dealwith the combinatorial explosion, caused by the enormous complexity of naturalimages, and obtain the rich understanding of visual scenes that the humanvisual achieves. We argue that this combinatorial explosion takes us into aregime where "big data is not enough" and where we need to rethink our methodsfor benchmarking performance and evaluating vision algorithms. We stress that,as vision algorithms are increasingly used in real world applications, thatperformance evaluation is not merely an academic exercise but has importantconsequences in the real world. It is impractical to review the entire Deep Netliterature so we restrict ourselves to a limited range of topics and referenceswhich are intended as entry points into the literature. The views expressed inthis paper are our own and do not necessarily represent those of anybody elsein the computer vision community.

Deep Nets: What have they ever done for Vision?