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.
|Title of host publication||Computer Vision – ECCV 2018 Workshops, Proceedings|
|Editors||Laura Leal-Taixé, Stefan Roth|
|Number of pages||10|
|Publication status||Published - 2019|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: 2018 Sep 8 → 2018 Sep 14
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th European Conference on Computer Vision, ECCV 2018|
|Period||18/9/8 → 18/9/14|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by National Research Foundation of Korea Grant funded by the Korea government (NRF-2016R1D1A1A09919650). This work was also funded by the Korea Meteorological Administration Research and Development Program under Grant KMIPA(KMI2018-06710).
© Springer Nature Switzerland AG 2019.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)