most citedVisual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Learning Concept Bottleneck Models from Mechanistic Explanations

Antonio De Santis, Schrasing Tong, Marco Brambilla +1

Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approac…

cs.LG2024

Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

Matteo Bianchi, Antonio De Santis, Andrea Tocchetti +1

Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainabi…