Efficient Optimization of Convolutional Neural Networks Using Particle Swarm Optimization

@article{Yamasaki2017EfficientOO,
  title={Efficient Optimization of Convolutional Neural Networks Using Particle Swarm Optimization},
  author={Toshihiko Yamasaki and Takuto Honma and Kiyoharu Aizawa},
  journal={2017 IEEE Third International Conference on Multimedia Big Data (BigMM)},
  year={2017},
  pages={70-73}
}
This work presents methods to automatically find optimal parameter settings for convolutional neural networks (CNNs) by using an evolutionary algorithm called particle swarm optimization (PSO). Even though the parameter space is extremely large (> 10 20), we experimentally show that a better parameter setting can be found for Alexnet configuration for five different image datasets. We have also developed two candidate pruning algorithms for efficient evolutionary process. In the experiments, we… CONTINUE READING
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