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Why do some continue to wonder about the success and dominance of deeplearning methods in computer vision and AI? Is it not enough that these methodsprovide practical solutions to many problems? Well no, it is not enough, atleast for those who feel there should be a science that underpins all of thisand that we should have a clear understanding of how this success was achieved.Here, this paper proposes that the dominance we are witnessing would not havebeen possible by the methods of deep learning alone: the tacit change has beenthe evolution of empirical practice in computer vision and AI over the pastdecades. We demonstrate this by examining the distribution of sensor settingsin vision datasets and performance of both classic and deep learning algorithmsunder various camera settings. This reveals a strong mismatch between optimalperformance ranges of classical theory-driven algorithms and sensor settingdistributions in the common vision datasets, while data-driven models weretrained for those datasets. The head-to-head comparisons between data-drivenand theory-driven models were therefore unknowingly biased against thetheory-driven models.

A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision