  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. We demonstrate higher compression ratio providing less
accuracy loss.
