Genetic Algorithm-Based Pruning for Efficient DenseNet Architectures.
International Conference on Artificial Intelligence in Information and Communication(2024)
摘要
CNNs have shown remarkable performance on a variety of computer vision problems. However, CNN-based models require a lot of computational resources, which have limitations of resource-constrained environments. To address this problem, various lightweight techniques have been developed, such as pruning of network structures. This paper employed a genetic algorithm (GA) to implement pruning with various pruning rates, aiming for the efficient DenseNet. We optimized the dense connectivity pattern of DenseNet-BC (
$k=12$
) using a GA-based pruning method with multi-dimensional encoding scheme. We demonstrate that the proposed method can perform similarly with fewer parameters than the baseline model.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要