Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels in different layers. Processing each layer independently fails to utilize the collaborative relationship across layers. In this paper, we intuitively propose a novel pruning method by explicitly leveraging the Filters Similarity in Consecutive Layers (FSCL). FSCL compresses models by pruning filters whose corresponding features are more worthless in the model. The extensive experiments demonstrate the effectiveness of FSCL, and it yields remarkable improvement over state-of-the-art on accuracy, FLOPs and parameter reduction on several benchmark models and datasets.

翻译：滤波器剪枝被广泛用于压缩和加速卷积神经网络(CNNs)，但以往大多数工作忽略了不同层中滤波器与通道之间的关联性。独立处理每一层无法充分利用跨层间的协作关系。本文提出了一种新颖的剪枝方法，通过显式利用连续层中的滤波器相似性(FSCL)来压缩模型。FSCL通过剪除其特征在模型中价值较低的滤波器来压缩模型。大量实验证明了FSCL的有效性，在多个基准模型和数据集上，该方法在准确率、FLOPs和参数缩减方面均优于现有最先进技术。