Standard
FCNN: Fourier Convolutional Neural Networks. / Pratt, Harry
; Williams, Bryan; Coenen, Frans et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. ed. / Michelangelo Ceci; Saso Dzeroski; Celine Vens; Ljupco Todorovski; Jaakko Hollmen. Springer-Verlag, 2017. p. 786-798 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10534 ).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Pratt, H
, Williams, B, Coenen, F & Zheng, Y 2017,
FCNN: Fourier Convolutional Neural Networks. in M Ceci, S Dzeroski, C Vens, L Todorovski & J Hollmen (eds),
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10534 , Springer-Verlag, pp. 786-798, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017, Skopje, Macedonia, The Former Yugoslav Republic of,
18/09/17.
https://doi.org/10.1007/978-3-319-71249-9_47
APA
Pratt, H.
, Williams, B., Coenen, F., & Zheng, Y. (2017).
FCNN: Fourier Convolutional Neural Networks. In M. Ceci, S. Dzeroski, C. Vens, L. Todorovski, & J. Hollmen (Eds.),
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings (pp. 786-798). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10534 ). Springer-Verlag.
https://doi.org/10.1007/978-3-319-71249-9_47
Vancouver
Pratt H
, Williams B, Coenen F, Zheng Y.
FCNN: Fourier Convolutional Neural Networks. In Ceci M, Dzeroski S, Vens C, Todorovski L, Hollmen J, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Springer-Verlag. 2017. p. 786-798. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71249-9_47
Author
Pratt, Harry
; Williams, Bryan ; Coenen, Frans et al. /
FCNN : Fourier Convolutional Neural Networks. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. editor / Michelangelo Ceci ; Saso Dzeroski ; Celine Vens ; Ljupco Todorovski ; Jaakko Hollmen. Springer-Verlag, 2017. pp. 786-798 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{3ba7a851e08e4030b0a3d7f96fe438e2,
title = "FCNN: Fourier Convolutional Neural Networks",
abstract = "The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.",
author = "Harry Pratt and Bryan Williams and Frans Coenen and Yalin Zheng",
year = "2017",
month = dec,
day = "30",
doi = "10.1007/978-3-319-71249-9_47",
language = "English",
isbn = "9783319712482",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "786--798",
editor = "Michelangelo Ceci and Saso Dzeroski and Celine Vens and Ljupco Todorovski and Jaakko Hollmen",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
}
RIS
TY - GEN
T1 - FCNN
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
AU - Pratt, Harry
AU - Williams, Bryan
AU - Coenen, Frans
AU - Zheng, Yalin
PY - 2017/12/30
Y1 - 2017/12/30
N2 - The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.
AB - The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.
U2 - 10.1007/978-3-319-71249-9_47
DO - 10.1007/978-3-319-71249-9_47
M3 - Conference contribution/Paper
AN - SCOPUS:85040241392
SN - 9783319712482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 786
EP - 798
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
A2 - Ceci, Michelangelo
A2 - Dzeroski, Saso
A2 - Vens, Celine
A2 - Todorovski, Ljupco
A2 - Hollmen, Jaakko
PB - Springer-Verlag
Y2 - 18 September 2017 through 22 September 2017
ER -