An evaluation of distillation deep learning network architecture

@article{Fujii2017AnEO,
  title={An evaluation of distillation deep learning network architecture},
  author={Yoshitaka Fujii and Takumi Ichimura},
  journal={2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA)},
  year={2017},
  pages={103-108}
}
Recently, Deep Learning models have come to be widely used for many real problems. Their results showed high classification capabilities. However, we must decide some parameters while searching a Deep Learning architecture with an optimal structure for a given data set. Moreover the equipment of the specified hardware such as GPU is required to make a forward calculation of neural network. If the simple network architecture has same power of the original Deep Learning, the use of the trained… CONTINUE READING