FANet: Factor analysis neural network

Abstract

A cascaded factor analysis network is proposed in this paper, which is suitable for extracting distributed semantic representations to various problems ranging from digit recognition and image classification to face recognition. There are two key points in this novel model: 1. simplify and accelerate the deep convolution networks with competitive accuracy even state-of-the-art for many general image tasks; 2. combine a statistical methodfactor analysis with neural networks for excellent automatically learning ability and abundant semantic information. Experiments on many benchmark visual datasets demonstrate that this simple network performs efficiently and effectively while attaining competitive accuracy to the current state-of-the-art methods.