# Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization

arXiv: https://arxiv.org/abs/2411.16901 · HTML: https://mlsanity.com/paper/2411.16901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://mlsanity.com/paper/1808.06866/) (2018)
- [EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis](https://mlsanity.com/paper/1905.05934/) (2019)
- [Neural Pruning via Growing Regularization](https://mlsanity.com/paper/2012.09243/) (2020)
- [Channel Pruning via Automatic Structure Search](https://mlsanity.com/paper/2001.08565/) (2020)
- [Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure](https://mlsanity.com/paper/1904.03837/) (2019)
- [Advancing Model Pruning via Bi-level Optimization](https://mlsanity.com/paper/2210.04092/) (2022)
- [Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity](https://mlsanity.com/paper/2309.06626/) (2023)
- [Complexity-Driven CNN Compression for Resource-constrained Edge AI](https://mlsanity.com/paper/2208.12816/) (2022)
- [WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural Networks](https://mlsanity.com/paper/2302.08185/) (2023)
- [Electrostatic Force Regularization for Neural Structured Pruning](https://mlsanity.com/paper/2411.11079/) (2024)

## Abstract (arXiv)

Structured pruning is a widely employed strategy for accelerating deep convolutional neural networks (DCNNs). However, existing methods often necessitate modifications to the original architectures, involve complex implementations, and require lengthy fine-tuning stages. To address these challenges, we propose a novel physics-inspired approach that integrates the concept of gravity into the training stage of DCNNs. In this approach, the gravity is directly proportional to the product of the masses of the convolution filter and the attracting filter, and inversely proportional to the square of the distance between them. We applied this force to the convolution filters, either drawing filters closer to the attracting filter (experiencing weaker gravity) toward non-zero weights or pulling filters farther away (subject to stronger gravity) toward zero weights. As a result, filters experiencing stronger gravity have their weights reduced to zero, enabling their removal, while filters under weaker gravity retain significant weights and preserve important information. Our method simultaneously optimizes the filter weights and ranks their importance, eliminating the need for complex implementations or extensive fine-tuning. We validated the proposed approach on popular DCNN architectures using the CIFAR dataset, achieving competitive results compared to existing methods.