IIRNet: A lightweight deep neural network using intensely inverted residuals for image recognition

作者：

Highlights：

• A lightweight and efficient convolutional neural network architecture is constructed.

• Intensely inverted residual and multi-scale low-redundancy convolutions are used to reduce the model size and complexity.

• The proposed network achieves comparable classification accuracy to the mainstream compact network architectures.

• Balanced performance is obtained on three challenging datasets.

摘要

•A lightweight and efficient convolutional neural network architecture is constructed.•Intensely inverted residual and multi-scale low-redundancy convolutions are used to reduce the model size and complexity.•The proposed network achieves comparable classification accuracy to the mainstream compact network architectures.•Balanced performance is obtained on three challenging datasets.

论文关键词：Convolutional neural network (CNN),Lightweight CNN,Image recognition,Low-redundancy,Model size,Computation complexity

论文评审过程：Received 10 October 2019, Accepted 20 October 2019, Available online 24 October 2019, Version of Record 6 November 2019.

论文官网地址：https://doi.org/10.1016/j.imavis.2019.10.005