Fuzzy Pruning for Compression of Convolutional Neural Networks.

Pruning can be used in Convolutional Neural Networks(CNNs) to reduce the consumption of computations. In the iteration of pruning, a fixed ratio of filters or weights are pruned after evaluating their importance according to a certain criterion, which has the risk of leading to mis-pruning when the distribution of the importance is imbalanced. We propose a strategy to assist pruning for CNNs at filter level. Firstly, in order to evaluate importance and non-importance about filters in the network, we design two fuzzy membership functions respectively, then we evaluate all filters by their membership function value, finally, α-cut set is employed to determine the ones to be pruned . Experimental results demonstrate the effectiveness of our strategy, since it can obtain more compression than the popular pruning algorithms at similar accuracy level.