학술논문
Genetic Algorithm-Based Pruning for Efficient DenseNet Architectures
Document Type
Conference
Author
Source
2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Artificial Intelligence in Information and Communication (ICAIIC), 2024 International Conference on. :644-647 Feb, 2024
Subject
Language
ISSN
2831-6983
Abstract
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.