2 citations · 2 across the 1 of their papers we have counts for
3 papers
Tell me why: Visual foundation models as self-explainable classifiers
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In…
Revisiting the robustness of post-hoc interpretability methods
Jiawen Wei, Hugues Turbé, Gianmarco Mengaldo
Post-hoc interpretability methods play a critical role in explainable artificial intelligence (XAI), as they pinpoint portions of data that a trained deep learning model deemed imp…
ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and…