Abstract
In this paper, we study the problem of designing efficient
convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels.
In addition to structured sparse kernels, low-rank kernels
and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions
(IGC) and its variants (e.g., Xception), has been attracting
increasing interests.
Motivated by the observation that the convolutions contained in a group convolution in IGC can be further decomposed in the same manner, we present a modularized building block, IGC-V2: interleaved structured sparse convolutions. It generalizes interleaved group convolutions, which
is composed of two structured sparse kernels, to the product of more structured sparse kernels, further eliminating
the redundancy. We present the complementary condition
and the balance condition to guide the design of structured
sparse kernels, obtaining a balance among three aspects:
model size, computation complexity and classification accuracy. Experimental results demonstrate the advantage on
the balance among these three aspects compared to interleaved group convolutions and Xception, and competitive
performance compared to other state-of-the-art architecture design methods