par García-Díaz, Antonio 
;Bersini, Hugues
Référence 2020 International Joint Conference on Neural Networks (IJCNN)(19-24 July 2020: Glasgow, UK), 2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Publication Publié, 2020-09-28
;Bersini, Hugues Référence 2020 International Joint Conference on Neural Networks (IJCNN)(19-24 July 2020: Glasgow, UK), 2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Publication Publié, 2020-09-28
Publication dans des actes
| Résumé : | This paper presents a newly developed self-structuring algorithm for generating convolutional neural networks, as well as the results of preliminary tests performed on it. The algorithm produces DenseNet and DenseNet-BC architectures layer by layer from scratch, at the same time as they are being trained. Experimental results for well-known image classification datasets (CIFAR-10 and SVHN) are promising. The accuracy levels of generated networks are not significantly different than those of prebuilt DenseNet and DenseNet-BC with similar topologies, and are approaching the state of the art for these datasets. |
