par García-Díaz, Antonio 
;Bersini, Hugues
Référence 2021 International Joint Conference on Neural Networks (IJCNN)(18-22 July 2021: Shenzhen, China), 2021 International Joint Conference on Neural Networks (IJCNN), 2021 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Publication Publié, 2021-09-20
;Bersini, Hugues Référence 2021 International Joint Conference on Neural Networks (IJCNN)(18-22 July 2021: Shenzhen, China), 2021 International Joint Conference on Neural Networks (IJCNN), 2021 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Publication Publié, 2021-09-20
Publication dans des actes
| Résumé : | This paper presents a novel and unconventional NAS method called DensEMANN, which can automatically build DenseNet and DenseNet-BC architectures layer by layer and kernel by kernel while they are being trained. DensEMANN is a complex method, divided into a macro-algorithm (which adds dense layers one by one to the network) and a micro-algorithm (which builds new layers by adding and/or pruning convolution kernels from them). Using the micro-algorithm alone, it is possible to build one-layer DenseNet trained on CIFAR-10 and SVHN with similar or greater accuracy levels than those obtained with equivalent prebuilt architectures. As for the macro-algorithm, its results are still unpredictable, and further research is needed before its automatically-generated DenseNet can compete with neural networks designed by humans. |
