# Filter Pruning using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2011.02389 · HTML: https://mlsanity.com/paper/2011.02389/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

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

- [Performance of ℓ1 Regularization for Sparse Convex Optimization](https://mlsanity.com/paper/2307.07405/) (2023)
- [Smooth Model Compression without Fine-Tuning](https://mlsanity.com/paper/2505.24469/) (2025)

## Abstract (arXiv)

Since the convolutional neural networks are often trained with redundant parameters, it is possible to reduce redundant kernels or filters to obtain a compact network without dropping the classification accuracy. In this paper, we propose a filter pruning method using the hierarchical group sparse regularization. It is shown in our previous work that the hierarchical group sparse regularization is effective in obtaining sparse networks in which filters connected to unnecessary channels are automatically close to zero. After training the convolutional neural network with the hierarchical group sparse regularization, the unnecessary filters are selected based on the increase of the classification loss of the randomly selected training samples to obtain a compact network. It is shown that the proposed method can reduce more than 50% parameters of ResNet for CIFAR-10 with only 0.3% decrease in the accuracy of test samples. Also, 34% parameters of ResNet are reduced for TinyImageNet-200 with higher accuracy than the baseline network.