# Iterative Low-Rank Approximation for CNN Compression

arXiv: https://arxiv.org/abs/1803.08995 · HTML: https://mlsanity.com/paper/1803.08995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition](https://mlsanity.com/paper/2309.12412/) (2023)
- [Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression](https://mlsanity.com/paper/2510.09696/) (2025)

## Abstract (arXiv)

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.