Autor segons l'article: Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec
Departament: Enginyeria Informàtica i Matemàtiques
Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi / Romaní Also, Santiago / Schwarz Schuler, Joao Paulo
Paraules clau: Efficientnet Deep learning Dcnn Dcn Computer vision Cnn Classification
Resum: EfficientNet is a recent Deep Convolutional Neural Network (DCNN) architecture intended to be proportionally extendible in depth, width and resolution. Through its variants, it can achieve state of the art accuracy on the ImageNet classification task as well as on other classical challenges. Although its name refers to its efficiency with respect to the ratio between outcome (accuracy) and needed resources (number of parameters, flops), we are studying a method to reduce the original number of trainable parameters by more than 84% while keeping a very similar degree of accuracy. Our proposal is to improve the pointwise (1x1) convolutions, whose number of parameters rapidly grows due to the multiplication of the number of filters by the number of input channels that come from the previous layer. Basically, our tweak consists in grouping filters into parallel branches, where each branch processes a fraction of the input channels. However, by doing so, the learning capability of the DCNN is degraded. To avoid this effect, we suggest inter-leaving the output of filters from different branches at intermediate layers of consecutive pointwise convolutions. Our experiments with the CIFAR-10 dataset show that our optimized EfficientNet has similar learning capacity to the original layout when training from scratch.
Àrees temàtiques: Medicina ii Interdisciplinar Información y documentación General o multidisciplinar Engenharias iv Engenharias iii Comunicació i informació Ciências agrárias i Artificial intelligence
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat joaopaulo.schwarz@estudiants.urv.cat santiago.romani@urv.cat domenec.puig@urv.cat
Identificador de l'autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0002-7582-0711 0000-0001-6673-9615 0000-0002-0562-4205
Data d'alta del registre: 2025-02-18
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Frontiers In Artificial Intelligence And Applications. 339 383-391
Referència de l'ítem segons les normes APA: Schwarz Schuler, Joao Paulo; Romani, Santiago; Abdel-Nasser, Mohamed; Rashwan, Hatem; Puig, Domenec (2021). Grouped Pointwise Convolutions Significantly Reduces Parameters in EfficientNet. Amsterdam: IOS Press
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2021
Tipus de publicació: Proceedings Paper