Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning
- https://doi.org/10.2991/ijcis.d.200907.001How to use a DOI?
- F-transform, Convolutional neural network, Deep learning, Interpretability
One of the current trends in the deep neural network technology consists in allowing a man–machine interaction and providing an explanation of network design and learning principles. In this direction, an experience with fuzzy systems is of great support. We propose our insight that is based on the particular theory of fuzzy (F)-transforms. Besides a theoretical explanation, we develop a new architecture of a deep neural network where the F-transform convolution kernels are used in the first two layers. Based on a series of experiments, we demonstrate the suitability of the F-transform-based deep neural network in the domain of image processing with the focus on recognition. Moreover, we support our insight by revealing the similarity between the F-transform and first-layer kernels in the most used deep neural networks.
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Vojtech Molek AU - Irina Perfilieva PY - 2020 DA - 2020/09 TI - Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning JO - International Journal of Computational Intelligence Systems SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200907.001 DO - https://doi.org/10.2991/ijcis.d.200907.001 ID - Molek2020 ER -