It is widely known that deep learning using a deep neural network (DNN) provides high performance in the fields of image recognition and image generation. On the other hand, DNN is composed of a huge number of filters, and the amount of information to express the filter coefficients becomes very large. In this paper, we propose a new method to reduce the amount of information for expressing DNN by adaptively using linear quantization and Lloyd-Max quantization for the weighting coefficients that compose DNN. As a result of the simulation experiments, it was found that the amount of information representing the DNN designed by CIFAR-10 and CIFAR100 can be reduced by up to 1/80 and 1/9, respectively, while maintaining the recognition accuracy.
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