DRF-DRC: dynamic receptive field and dense residual connections for model compression

Deep convolutional neural networks have achived remarkable progress on computer vision tasks over last years. These novel neural architecture are most designed manually by human experts, which is a time-consuming process and not the best solution. Hence neural architecture search (NAS) has become a hot research topic for the design of neural architecture. In this paper, we propose the dynamic receptive field (DRF) operation and measurable dense residual connections (DRC) in search space for designing efficient networks, i.e., DRENet. The search method can be deployed on the MobileNetV2-based search space. The experimental results on CIFAR10/100, SVHN, CUB-200-2011, ImageNet and COCO benchmark datasets and an application example in a railway intelligent surveillance system demonstrate the effectiveness of our scheme, which achieves superior performance.

Automated summary

This paper proposes a novel method for neural architecture search, using dynamic receptive field and measurable dense residual connections to design efficient networks. Experimental results demonstrate the superior performance of this method in various computer vision tasks and applications.