Title: Locally Smoothed Neural Networks,
Abstract: Convolutional Neural Networks (CNN) and the locally connected layer are
limited in capturing the importance and relations of different local receptive
fields, which are often crucial for tasks such as face verification, visual
question answering, and word sequence prediction. To tackle the issue, we
propose a novel locally smoothed neural network (LSNN) in this paper. The main
idea is to represent the weight matrix of the locally connected layer as the
product of the kernel and the smoother, where the kernel is shared over
different local receptive fields, and the smoother is for determining the
importance and relations of different local receptive fields. Specifically, a
multi-variate Gaussian function is utilized to generate the smoother, for
modeling the location relations among different local receptive fields.
Furthermore, the content information can also be leveraged by setting the mean
and precision of the Gaussian function according to the content. Experiments on
some variant of MNIST clearly show our advantages over CNN and locally
connected layer.