  This paper introduces a new aspect for determining the rank of the
unimportant filters for filter pruning on convolutional neural networks (CNNs).
In the human synaptic system, there are two important channels known as
excitatory and inhibitory neurotransmitters that transmit a signal from a
neuron to a cell. Adopting the neuroscientific perspective, we propose a
synapse-inspired filter pruning method, namely Dynamic Score (D-Score). D-Score
analyzes the independent importance of positive and negative weights in the
filters and ranks the independent importance by assigning scores. Filters
having low overall scores, and thus low impact on the accuracy of neural
networks are pruned. The experimental results on CIFAR-10 and ImageNet datasets
demonstrate the effectiveness of our proposed method by reducing notable
amounts of FLOPs and Params without significant Acc. Drop.
