7 citations · 8 across the 3 of their papers we have counts for
4 papers
Focus! Rating XAI Methods and Finding Biases
Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla +1
AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature;…
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla +4
In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experi…
MetH: A family of high-resolution and variable-shape image challenges
Ferran Parés, Dario Garcia-Gasulla, Harald Servat +2
High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks…
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés +5
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 resour…