Radial basis function networks for convolutional neural networks to  learn similarity distance metric and improve interpretability

Amirian, Mohammadreza; Schwenker, Friedhelm

doi:10.1109/ACCESS.2020.3007337

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26284

Full metadata record

DC Field	Value	Language
dc.contributor.author	Amirian, Mohammadreza	-
dc.contributor.author	Schwenker, Friedhelm	-
dc.date.accessioned	2022-12-02T14:02:44Z	-
dc.date.available	2022-12-02T14:02:44Z	-
dc.date.issued	2020	-
dc.identifier.issn	2169-3536	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/26284	-
dc.description.abstract	Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern architectures. In this paper, we adapt RBF networks as a classifier on top of CNNs by modifying the training process and introducing a new activation function to train modern vision architectures end-to-end for image classification. The specific architecture of RBFs enables the learning of a similarity distance metric to compare and find similar and dissimilar images. Furthermore, we demonstrate that using an RBF classifier on top of any CNN architecture provides new human-interpretable insights about the decision-making process of the models. Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets.	de_CH
dc.language.iso	en	de_CH
dc.publisher	IEEE	de_CH
dc.relation.ispartof	IEEE Access	de_CH
dc.rights	http://creativecommons.org/licenses/by/4.0/	de_CH
dc.subject	Radial basis function neural network (RBF)	de_CH
dc.subject	Convolutional neural network (CNN)	de_CH
dc.subject	CNN-RBF	de_CH
dc.subject	Supervised learning	de_CH
dc.subject	Unsupervised learning	de_CH
dc.subject	Similarity distance metric	de_CH
dc.subject.ddc	006: Spezielle Computerverfahren	de_CH
dc.title	Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability	de_CH
dc.type	Beitrag in wissenschaftlicher Zeitschrift	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
dc.identifier.doi	10.1109/ACCESS.2020.3007337	de_CH
dc.identifier.doi	10.21256/zhaw-26284	-
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	123097	de_CH
zhaw.pages.start	123087	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.volume	8	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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Amirian, M., & Schwenker, F. (2020). Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability. IEEE Access, 8, 123087–123097. https://doi.org/10.1109/ACCESS.2020.3007337

Amirian, M. and Schwenker, F. (2020) ‘Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability’, IEEE Access, 8, pp. 123087–123097. Available at: https://doi.org/10.1109/ACCESS.2020.3007337.

M. Amirian and F. Schwenker, “Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability,” IEEE Access, vol. 8, pp. 123087–123097, 2020, doi: 10.1109/ACCESS.2020.3007337.

AMIRIAN, Mohammadreza und Friedhelm SCHWENKER, 2020. Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability. IEEE Access. 2020. Bd. 8, S. 123087–123097. DOI 10.1109/ACCESS.2020.3007337

Amirian, Mohammadreza, and Friedhelm Schwenker. 2020. “Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability.” IEEE Access 8: 123087–97. https://doi.org/10.1109/ACCESS.2020.3007337.

Amirian, Mohammadreza, and Friedhelm Schwenker. “Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability.” IEEE Access, vol. 8, 2020, pp. 123087–97, https://doi.org/10.1109/ACCESS.2020.3007337.