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.

使用神经科学的视角，通过分析滤波器中正负权重的独立重要性并赋予分数来确定卷积神经网络（CNN）中不重要的滤波器的排名，从而实现了一种新的滤波器裁剪方法D-Score。在CIFAR-10和ImageNet数据集上的实验证明了该方法的有效性，可以显著减少FLOPs和参数数量而不会对准确性造成重大影响。

D-Score：一种基于突触启发的滤波器修剪方法