Evolutionary Computation Based CNN Filter Reduction

진화연산 기반 CNN 필터 축소

  • Seo, Kisung
  • 서기성
  • Received : 2018.11.01
  • Accepted : 2018.11.29
  • Published : 2018.12.01


A convolutional neural network (CNN), which is one of the deep learning models, has been very successful in a variety of computer vision tasks. Filters of a CNN are automatically generated, however, they can be further optimized since there exist the possibility of existing redundant and less important features. Therefore, the aim of this paper is a filter reduction to accelerate and compress CNN models. Evolutionary algorithms is adopted to remove the unnecessary filters in order to minimize the parameters of CNN networks while maintaining a good performance of classification. We demonstrate the proposed filter reduction methods performing experiments on CIFAR10 data based on the classification performance. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.


Convolutional neural network;Filter reduction;Genetic algorithm


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