Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi, Reza Anvari, Ali Forghani, Behrouz Minaei Bidgoli · Signal & Image Processing An International Journal · 2019
It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost.Also, input instances have not the same difficulty.As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolutional neural networks.This method trains a CNN with a set of auxiliary outputs and expert branches in some middle layers of the network.The expert branches decide to use a shallower part of the network or going deeper to the end, based on the difficulty of input instance.The expert branches learn to determine: is the current network prediction is wrong and if the given instance passed to deeper layers of the network it will generate right output; If not, then the expert branches stop the computation process.The experimental results on standard dataset CIFAR-10 show that the proposed method can train models with lower test-cost and competitive accuracy in comparison with basic models.