Fast Inference Predictive Coding: A Novel Model for Constructing Deep Neural Networks

作     者：Song, Zengjie Zhang, Jiangshe Shi, Guang Liu, Junmin 

作者机构：Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2019年第30卷第4期

页      面：1150-1165页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Basic Research Program of China (973 Program) [2013CB329404] National Natural Science Foundation of China [61572393, 11671317, 11401465] National Key Research and Development Program of China [2018YFC0809001] 

主　　题：Deep learning (DL) feature extraction image classification inference mechanisms predictive coding (PC) 

摘      要：As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development of PC model is encouraging in the neurobiology community, its practical applications in machine learning (e.g., image classification) have not been fully explored yet. In this paper, a novel image processing model called fast inference PC (FIPC) is presented for image representation and classification. Compared with the basic PC model, a regression procedure and a classification layer have been added to the proposed FIPC model. The regression procedure is used to learn regression mappings that achieve fast inference at test time, while the classification layer can instruct the model to extract more discriminative features. In addition, effective learning and fine-tuning algorithms are developed for the proposed model. Experimental results obtained on four image benchmark data sets show that our model is able to directly and fast infer representations and, simultaneously, produce lower error rates on image classification tasks.