no code implementations • 29 Sep 2021 • Mario Parreño Lara, Roberto Paredes, Alberto Albiol
Domain adaptation techniques aim to fill this gap by generating mappings between image domains when unlabeled data from the new target domain is available.
1 code implementation • 22 Mar 2020 • Juan Maroñas, Daniel Ramos, Roberto Paredes
Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN.
1 code implementation • 23 Aug 2019 • Juan Maroñas, Roberto Paredes, Daniel Ramos
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks.
no code implementations • 21 Mar 2019 • Juan Maroñas, Roberto Paredes, Daniel Ramos
The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail.
no code implementations • 27 Sep 2018 • Juan Maroñas, Roberto Paredes, Daniel Ramos
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks.
1 code implementation • Pattern Recognition 2018 • Javier Jorge, Roberto Paredes
Nowadays, there is an increasing demand for machine learning techniques which can deal with problems where the instances are produced as a stream or in real time.
no code implementations • 5 Oct 2016 • Roberto Paredes, José-Miguel Benedí
Layers is an open source neural network toolkit aim at providing an easy way to implement modern neural networks.