期刊名称:Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN:2229-3922
电子版ISSN:0976-710X
出版年度:2019
卷号:10
期号:5
页码:1-13
DOI:10.5121/sipij.2019.10502
出版社:Academy & Industry Research Collaboration Center (AIRCC)
摘要:It has been proven than 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..
关键词:Test;Cost;Sensitive Learning; Deep Learning; CNN withExpert Branches; Instance;Based Cost