Distillation技術を用いたネットワークの分類精度に対する考察

Abstract—Since the architecture of deep learning has many parameters such as weights, bias, and its structure, we may be faced with a problem that the learning technology can not be implemented in a real world. The general deep learning method takes a long time calculation. Therefore, a new technology for the simplification of architecture is required. The trained simplified network should reach the higher classification capability under the optimal structure. In this paper, we investigate the Distillation technology proposed by Hinton using AIC to evaluate the trained network architecture and its accuracy of classification.開催日：平成29年7月22日 会場：広島工業大