Iterative Low-Rank Approximation for CNN Compression

Understand

Deep convolutional neural networks contain tens of millions of parameters, making them impossible to work efficiently on embedded devices.

• We propose iterative approach of applying low-rank approximation to compress deep convolutional neural networks.
• Since classification and object detection are the most favored tasks for embedded devices, we demonstrate the effectiveness of our approach by compressing AlexNet, VGG-16, YOLOv2 and Tiny YOLO networks.
• Our results show the superiority of the proposed method compared to non-repetitive ones.