  The advancement of convolutional neural networks (CNNs) on various vision
applications has attracted lots of attention. Yet the majority of CNNs are
unable to satisfy the strict requirement for real-world deployment. To overcome
this, the recent popular network pruning is an effective method to reduce the
redundancy of the models. However, the ranking of filters according to their
"importance" on different pruning criteria may be inconsistent. One filter
could be important according to a certain criterion, while it is unnecessary
according to another one, which indicates that each criterion is only a partial
view of the comprehensive "importance". From this motivation, we propose a
novel framework to integrate the existing filter pruning criteria by exploring
the criteria diversity. The proposed framework contains two stages: Criteria
Clustering and Filters Importance Calibration. First, we condense the pruning
criteria via layerwise clustering based on the rank of "importance" score.
Second, within each cluster, we propose a calibration factor to adjust their
significance for each selected blending candidates and search for the optimal
blending criterion via Evolutionary Algorithm. Quantitative results on the
CIFAR-100 and ImageNet benchmarks show that our framework outperforms the
state-of-the-art baselines, regrading to the compact model performance after
pruning.
