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.

翻译：本文提出了一种新的视角，用于确定卷积神经网络（CNNs）中不重要滤波器的排序。在人类突触系统中，存在两种重要的通道——兴奋性神经递质和抑制性神经递质，它们负责将信号从神经元传递到细胞。借鉴神经科学的观点，我们提出了一种受突触启发的滤波器剪枝方法，即动态评分（D-Score）。D-Score通过分析滤波器中正负权重的独立重要性，并分配评分来对独立重要性进行排序。那些整体评分较低、因此对神经网络精度影响较小的滤波器会被剪枝。在CIFAR-10和ImageNet数据集上的实验结果表明，我们提出的方法在减少显著数量的FLOPs和参数的同时，未导致明显的精度下降，从而验证了其有效性。