How deep should be the depth of convolutional neural networks: a backyard dog case study

We present a straightforward non-iterative method for shallowing of deep Convolutional Neural Network (CNN) by combination of several layers of CNNs with Advanced Supervised Principal Component Analysis (ASPCA) of their outputs. We tested this new method on a practically important case of'friend-or-foe' face recognition. This is the backyard dog problem: the dog should (i) distinguish the members of the family from possible strangers and (ii) identify the members of the family. Our experiments revealed that the method is capable of drastically reducing the depth of deep learning CNNs, albeit at the cost of mild performance deterioration. 1. Introduction IT giants have produced many software "semiproducts" for image recognition. This new opportunity gave rise to many works in face recognition. These works and popular critics of their results prove that the performance of these systems are problem-depending and the devil is in the detail of testing and validation: the systems, which are almost perfect for one problem can be useless for another one. In this paper we focus on a problem which, on the one hand, appears to be a close relative of the face recognition applications and yet, on the other hand, is somewhat more relaxed.