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- Title
Lowering mutual coherence between receptive fields in convolutional neural networks.
- Authors
Amini, S.; Ghaemmaghami, S.
- Abstract
It has been shown that more accurate signal recovery can be achieved with low-coherence dictionaries in sparse signal processing. In this Letter, the authors extend the low-coherence attribute to receptive fields in convolutional neural networks. A new constrained formulation to train low-coherence convolutional neural network is presented and an efficient algorithm is proposed to train the network. The resulting formulation produces a direct link between the receptive fields of a layer through training procedure that can be used to extract more informative representations from the subsequent layers. Simulation results over three benchmark datasets confirm superiority of the proposed low-coherence convolutional neural network over the unconstrained version.
- Subjects
ARTIFICIAL neural networks; SIGNAL processing; DEEP learning; MATHEMATICAL optimization; MEAN square algorithms
- Publication
Electronics Letters (Wiley-Blackwell), 2019, Vol 55, Issue 6, p325
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/el.2018.7671