3 papers
cs.CV2026
A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2
Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking inter…
cs.CV2024
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Antonio De Santis, Riccardo Campi, Matteo Bianchi +1
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexit…
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…