activity
20172020
most citedDeep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery

70 citations · 128 across the 5 of their papers we have counts for

collaborators

7 papers

cs.NE2020

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez +5

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parame…

cs.CV202017 cited

FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations

Manuel Rey-Area, Emilio Guirado, Siham Tabik +1

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This iss…

cs.LG2020

MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1 error rate. Ensembles overview and proposal

S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz +3

Ensemble methods have been widely used for improving the results of the best single classificationmodel. A large body of works have achieved better performance mainly by applying o…

cs.AI201939 cited

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser +9

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this t…

cs.CV2019

Deep Learning in Video Multi-Object Tracking: A Survey

Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik +3

The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learn…

cs.CV201770 cited

Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery

Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura +2

There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been perfo…