1 citations · 1 across the 7 of their papers we have counts for
7 papers
Discovering shared interpretable operations in image compression autoencoders
Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey
With the increasing adoption of deep learning for applications such as image compression, improvements in the rate-distortion trade-off have been achieved at the cost of increasing…
Efficient training for compact compression models via sequential distillation
Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey
Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high-quality reconstructions, they a…
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…
Unsupervised discovery of Interpretable Visual Concepts
Caroline Mazini Rodrigues, Nicolas Boutry, Laurent Najman
Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such…