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|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability|
|Published in:||IEEE Access|
|Publisher / Ed. Institution:||IEEE|
|Subjects:||Radial basis function neural network (RBF); Convolutional neural network (CNN); CNN-RBF; Supervised learning; Unsupervised learning; Similarity distance metric|
|Subject (DDC):||006: Special computer methods|
|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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Information Technology (InIT)|
|Appears in collections:||Publikationen School of Engineering|
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|2020_Amirian-Schwenker_Radial-basis-function-networks-learn-similarity-distance-metric-improve-interpretability.pdf||7.85 MB||Adobe PDF|
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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, 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.
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