  Channel pruning has received ever-increasing focus on network compression. In
particular, class-discrimination based channel pruning has made major headway,
as it fits seamlessly with the classification objective of CNNs and provides
good explainability. Prior works singly propose and evaluate their discriminant
functions, while further study on the effectiveness of the adopted metrics is
absent. To this end, we initiate the first study on the effectiveness of a
broad range of discriminant functions on channel pruning. Conventional
single-variate binary-class statistics like Student's T-Test are also included
in our study via an intuitive generalization. The winning metric of our study
has a greater ability to select informative channels over other
state-of-the-art methods, which is substantiated by our qualitative and
quantitative analysis. Moreover, we develop a FLOP-normalized sensitivity
analysis scheme to automate the structural pruning procedure. On CIFAR-10,
CIFAR-100, and ILSVRC-2012 datasets, our pruned models achieve higher accuracy
with less inference cost compared to state-of-the-art results. For example, on
ILSVRC-2012, our 44.3% FLOPs-pruned ResNet-50 has only a 0.3% top-1 accuracy
drop, which significantly outperforms the state of the art.
