# A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

arXiv: https://arxiv.org/abs/2004.05531 · HTML: https://mlsanity.com/paper/2004.05531/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

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

- [Accelerating ViT Inference on FPGA through Static and Dynamic Pruning](https://mlsanity.com/paper/2403.14047/) (2024)
- [Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation](https://mlsanity.com/paper/2605.26382/) (2026)

## Abstract (arXiv)

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i.e., static regularization-based pruning and dynamic regularization-based pruning. However, the former method currently suffers either complex workloads or accuracy degradation, while the latter one takes a long time to tune the parameters to achieve the desired pruning rate without accuracy loss. In this paper, we propose a unified DNN weight pruning framework with dynamically updated regularization terms bounded by the designated constraint, which can generate both non-structured sparsity and different kinds of structured sparsity. We also extend our method to an integrated framework for the combination of different DNN compression tasks.