9 citations · 26 across the 6 of their papers we have counts for
9 papers
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
Adrien Bennetot, Gianni Franchi, Javier Del Ser +2
Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep l…
A study of deep perceptual metrics for image quality assessment
Rémi Kazmierczak, Gianni Franchi, Nacim Belkhir +2
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images. In this work, we propo…
Robust Semantic Segmentation with Superpixel-Mix
Gianni Franchi, Nacim Belkhir, Mai Lan Ha +4
Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertain…
Learning a Discriminant Latent Space with Neural Discriminant Analysis
Mai Lan Ha, Gianni Franchi, Emanuel Aldea +1
Discriminative features play an important role in image and object classification and also in other fields of research such as semi-supervised learning, fine-grained classification…
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez +7
The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are…
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. Whi…