1 code implementation • 27 Jul 2020 • Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla, Gema Campo-Francés, Nina Viladrich, Eduard Ayguadé, Jesús Labarta
In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties.
Ranked #1 on Image Classification on MAMe
1 code implementation • 28 Sep 2021 • Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla, Victor Gimenez-Abalos
In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
2 code implementations • 27 Mar 2017 • Ferran Parés, Dario Garcia-Gasulla, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
1 code implementation • 20 Nov 2019 • Ferran Parés, Dario Garcia-Gasulla, Harald Servat, Jesús Labarta, Eduard Ayguadé
In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH).
no code implementations • 3 Mar 2017 • Dario Garcia-Gasulla, Ferran Parés, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.
no code implementations • WS 2017 • Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
no code implementations • WS 2017 • Armand Vilalta, Dario Garcia-Gasulla, Ferran Parés, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
no code implementations • ICLR 2018 • Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.
no code implementations • 26 Nov 2019 • Raquel Pérez-Arnal, Dario Garcia-Gasulla, David Torrents, Ferran Parés, Ulises Cortés, Jesús Labarta, Eduard Ayguadé
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.