  Channel pruning is a popular technique for compressing convolutional neural
networks (CNNs), where various pruning criteria have been proposed to remove
the redundant filters. From our comprehensive experiments, we found two blind
spots in the study of pruning criteria: (1) Similarity: There are some strong
similarities among several primary pruning criteria that are widely cited and
compared. According to these criteria, the ranks of filters'Importance Score
are almost identical, resulting in similar pruned structures. (2)
Applicability: The filters'Importance Score measured by some pruning criteria
are too close to distinguish the network redundancy well. In this paper, we
analyze these two blind spots on different types of pruning criteria with
layer-wise pruning or global pruning. The analyses are based on the empirical
experiments and our assumption (Convolutional Weight Distribution Assumption)
that the well-trained convolutional filters each layer approximately follow a
Gaussian-alike distribution. This assumption has been verified through
systematic and extensive statistical tests.
