1 citations · 1 across the 4 of their papers we have counts for
4 papers
Found in Translation: semantic approaches for enhancing AI interpretability in face verification
Miriam Doh, Caroline Mazini Rodrigues, N. Boutry +3
The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to…
Explaning with trees: interpreting CNNs using hierarchies
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
Challenges persist in providing interpretable explanations for neural network reasoning in explainable AI (xAI). Existing methods like Integrated Gradients produce noisy maps, and…
Bridging Human Concepts and Computer Vision for Explainable Face Verification
Miriam Doh, Caroline Mazini Rodrigues, Nicolas Boutry +3
With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairnes…
Transforming gradient-based techniques into interpretable methods
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
The explication of Convolutional Neural Networks (CNN) through xAI techniques often poses challenges in interpretation. The inherent complexity of input features, notably pixels ex…