  Recent results have revealed an interesting observation in a trained
convolutional neural network (CNN), namely, the rank of a feature map channel
matrix remains surprisingly constant despite the input images. This has led to
an effective rank-based channel pruning algorithm, yet the constant rank
phenomenon remains mysterious and unexplained. This work aims at demystifying
and interpreting such rank behavior from a frequency-domain perspective, which
as a bonus suggests an extremely efficient Fast Fourier Transform (FFT)-based
metric for measuring channel importance without explicitly computing its rank.
We achieve remarkable CNN channel pruning based on this analytically sound and
computationally efficient metric and adopt it for repetitive pruning to
demonstrate robustness via our scheme named Energy-Zoned Channels for Robust
Output Pruning (EZCrop), which shows consistently better results than other
state-of-the-art channel pruning methods.
