CLUSTERING ALGORITHM NETWORKS TEST COST SENSITIVE FOR SPECIALIST DIVISIONS

Abstract
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 convolution neural networks. This method trains a CNN with a set Based on the difficulty of the input instance, the expert branches decide to use a shallower part of the network or go deeper to the end. The expert branches learn to determine: is the current network prediction wrong and if the instance passed to deeper network layers it will generate the right output; if not, then the expert branches will stop the process of computation. The experimental results on the standard CIFAR-10 dataset indicate that in comparison with basic models, the proposed method can train models with lower test cost and competitive accuracy.

Authors
M Arvindhan1, G S Pradeep Ghantasala2, N V Kousik3
Galgotias University, India1,3, Malla Reddy Institute of Technology and Science, India2

Keywords
Test-Cost-Sensitive Learning, Deep Learning, CNN with Expert Branches, Instance-based Cost
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 10 , Issue: 2 )
Date of Publication :
November 2019

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