# Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers

arXiv: https://arxiv.org/abs/1902.06382 · HTML: https://mlsanity.com/paper/1902.06382/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Model Compression Methods for YOLOv5: A Review](https://mlsanity.com/paper/2307.11904/) (2023)
- [RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs](https://mlsanity.com/paper/2103.08457/) (2021)

## Abstract (arXiv)

Channel pruning has been identified as an effective approach to constructing efficient network structures. Its typical pipeline requires iterative pruning and fine-tuning. In this work, we propose a novel single-shot channel pruning approach based on alternating direction methods of multipliers (ADMM), which can eliminate the need for complex iterative pruning and fine-tuning procedure and achieve a target compression ratio with only one run of pruning and fine-tuning. To the best of our knowledge, this is the first study of single-shot channel pruning. The proposed method introduces filter-level sparsity during training and can achieve competitive performance with a simple heuristic pruning criterion (L1-norm). Extensive evaluations have been conducted with various widely-used benchmark architectures and image datasets for object classification purpose. The experimental results on classification accuracy show that the proposed method can outperform state-of-the-art network pruning works under various scenarios.