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Recent results have revealed an interesting observation in a trainedconvolutional neural network (CNN), namely, the rank of a feature map channelmatrix remains surprisingly constant despite the input images. This has led toan effective rank-based channel pruning algorithm, yet the constant rankphenomenon remains mysterious and unexplained. This work aims at demystifyingand interpreting such rank behavior from a frequency-domain perspective, whichas a bonus suggests an extremely efficient Fast Fourier Transform (FFT)-basedmetric for measuring channel importance without explicitly computing its rank.We achieve remarkable CNN channel pruning based on this analytically sound andcomputationally efficient metric and adopt it for repetitive pruning todemonstrate robustness via our scheme named Energy-Zoned Channels for RobustOutput Pruning (EZCrop), which shows consistently better results than otherstate-of-the-art channel pruning methods.

EZCrop: Energy-Zoned Channels for Robust Output Pruning