# MUSCO: Multi-Stage Compression of neural networks

arXiv: https://arxiv.org/abs/1903.09973 · HTML: https://mlsanity.com/paper/1903.09973/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization](https://mlsanity.com/paper/2309.03824/) (2023)
- [tntorch: Tensor Network Learning with PyTorch](https://mlsanity.com/paper/2206.11128/) (2022)
- [Tensor Yard: One-Shot Algorithm of Hardware-Friendly Tensor-Train Decomposition for Convolutional Neural Networks](https://mlsanity.com/paper/2108.04029/) (2021)
- [Accelerating the Low-Rank Decomposed Models](https://mlsanity.com/paper/2407.20266/) (2024)
- [Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks](https://mlsanity.com/paper/2511.04494/) (2025)

## Abstract (arXiv)

The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our approach improves the compression rate while maintaining the accuracy for a variety of tasks.