  Deep learning revolutionized data science, and recently its popularity has
grown exponentially, as did the amount of papers employing deep networks.
Vision tasks, such as human pose estimation, did not escape from this trend.
There is a large number of deep models, where small changes in the network
architecture, or in the data pre-processing, together with the stochastic
nature of the optimization procedures, produce notably different results,
making extremely difficult to sift methods that significantly outperform
others. This situation motivates the current study, in which we perform a
systematic evaluation and statistical analysis of vanilla deep regression, i.e.
convolutional neural networks with a linear regression top layer. This is the
first comprehensive analysis of deep regression techniques. We perform
experiments on four vision problems, and report confidence intervals for the
median performance as well as the statistical significance of the results, if
any. Surprisingly, the variability due to different data pre-processing
procedures generally eclipses the variability due to modifications in the
network architecture. Our results reinforce the hypothesis according to which,
in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately
tuned can yield results close to the state-of-the-art without having to resort
to more complex and ad-hoc regression models.
