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

9 citations · 17 across the 9 of their papers we have counts for

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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★ 9 cited

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…