GA-Based Filter Selection for Representation in Convolutional Neural Networks
Abstract
One of the deep learning models, a convolutional neural network (CNN) has been very successful in a variety of computer vision tasks. Features of a CNN are automatically generated, however, they can be further optimized since they often require large scale parallel operations and there exist the possibility of overlapping redundant features. The aim of this paper is to use feature selection via evolutionary algorithms to remove the irrelevant deep features. This will minimize the computational complexity and the amount of overfitting while maintaining a good quality of representation. We demonstrate the improvement of the filter representation by performing experiments on three data sets of CIFAR10, metal surface defects, and variation of MNIST and by analyzing the classification performance as well as the variance of the filter.
Cite
Text
Kim et al. "GA-Based Filter Selection for Representation in Convolutional Neural Networks." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11018-5_48Markdown
[Kim et al. "GA-Based Filter Selection for Representation in Convolutional Neural Networks." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/kim2018eccvw-gabased/) doi:10.1007/978-3-030-11018-5_48BibTeX
@inproceedings{kim2018eccvw-gabased,
title = {{GA-Based Filter Selection for Representation in Convolutional Neural Networks}},
author = {Kim, Junbong and Lee, Minki and Choi, Jongeun and Seo, Kisung},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {609-618},
doi = {10.1007/978-3-030-11018-5_48},
url = {https://mlanthology.org/eccvw/2018/kim2018eccvw-gabased/}
}