Deep Coupling of Random Ferns.

Sangwon Kim, Mira Jeong, Deokwoo Lee, ByoungChul Ko

Abstract: The purpose of this study is to design a new lightweight explainable deep model instead of deep neural networks (DNN) because of its high memory and processing resource requirement as well as black-box training although DNN is a powerful algorithm for classification and regression problems. This study propose a non-neural network style deep model based on combination of deep coupling random ferns (DCRF). In proposed DCRF, each neuron of a layer is replaced with the Fern and each layer consists of several type of Ferns. The proposed method showed a higher uniform performance in terms of the number of parameters and operations without a loss of accuracy compared to a few related studies including a DNN based model compression algorithm.