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In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to fully exploit many deep neural networks (DNNs), especially in scenarios in which computing resources are limited. In this paper, to tackle this problem, we introduce a method fordoi:10.3390/s20216033 pmid:33114078 pmcid:PMC7660330 fatcat:hetsfskkifdlbhdgkbdh66ked4