# A novel channel pruning method for deep neural network compression

arXiv: https://arxiv.org/abs/1805.11394 · HTML: https://mlsanity.com/paper/1805.11394/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 8 of 8)

- [Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN](https://mlsanity.com/paper/2107.14444/) (2021)
- [Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction](https://mlsanity.com/paper/2112.15445/) (2021)
- [Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving](https://mlsanity.com/paper/2402.05146/) (2024)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting](https://mlsanity.com/paper/2308.07939/) (2023)
- [Compact Neural Networks via Stacking Designed Basic Units](https://mlsanity.com/paper/2205.01508/) (2022)
- [Embedding Compression for Text Classification Using Dictionary Screening](https://mlsanity.com/paper/2211.12715/) (2022)
- [Lightweight Design and Optimization methods for DCNNs: Progress and Futures](https://mlsanity.com/paper/2412.16886/) (2024)

## Abstract (arXiv)

In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-constrained embedded devices such as mobile robots, smart phones and so on. Therefore, network compression for such platforms is a reasonable solution to reduce memory consumption and computation complexity. In this paper, a novel channel pruning method based on genetic algorithm is proposed to compress very deep Convolution Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy drop. Channel selection is a combinatorial optimization problem that has exponential solution space. In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm. Meanwhile, a two-step approximation fitness function is designed to further improve the efficiency of genetic process. The proposed method has been verified on three benchmark datasets with two popular CNN models: VGGNet and ResNet. On the CIFAR-100 and ImageNet datasets, our approach outperforms several state-of-the-art methods. On the CIFAR-10 and SVHN datasets, the pruned VGGNet achieves better performance than the original model with 8 times parameters compression and 3 times FLOPs reduction.