Equivalent Scanning Network of Unpadded CNNs
Journal
IEEE Signal Processing Letters
Journal Volume
25
Journal Issue
10
Pages
1590-1594
Date Issued
2018
Author(s)
Abstract
This letter presents a theory of scanning a signal with a sliding window, where the window's mapping function is built upon a convolutional neural network (CNN). When using a CNN as the sliding window, we show that the resultant feature maps are equivalent to the maps obtained by applying another CNN (called EQ-ScanNet) to the whole signal. The EQ-ScanNet can be established by reconfiguring the original CNN with dilated (i.e., sparse kernel) convolutions. We clarify that, this property is originated from the noble identity (i.e., the swapping equivalence of downsample and FIR filter), and extend the property to the generalized convolution that subsumes CNN's window-sliding operations. We further show that an unpadded CNN is a necessary condition for formulating the EQ-ScanNet. ? 1994-2012 IEEE.
Subjects
FIR filters; Neural networks; CNNs; Convolutional Neural Networks (CNN); Feature map; Mapping functions; Noble identity; Sliding Window; Sparse kernels; Window sliding; Convolution
Type
journal article