TY - GEN
T1 - GA-based filter selection for representation in convolutional neural networks
AU - Kim, Junbong
AU - Lee, Minki
AU - Choi, Jongeun
AU - Seo, Kisung
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061737419&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061737419&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11018-5_48
DO - 10.1007/978-3-030-11018-5_48
M3 - Conference contribution
AN - SCOPUS:85061737419
SN - 9783030110178
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 609
EP - 618
BT - Computer Vision – ECCV 2018 Workshops, Proceedings
A2 - Leal-Taixé, Laura
A2 - Roth, Stefan
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -