Welcome to the official TensorFlow V2 implementation of Visual-TCAV, a concept-based framework for post-hoc explainability in image classification.
In this repository you will find all the necessary items to run the method:
| File/folder | Description |
|---|---|
| VisualTCAV_guide.ipynb | Detailed guide on how to install and run Visual-TCAV |
| VisualTCAV.py | Visual-TCAV code |
| datasets_and_models_downloader | Code and files to automatically download the datasets and the models |
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexity of these models. State-of-the-art saliency methods generate local explanations highlighting the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction. On the other hand, concept-based methods, such as TCAV, provide insights into how sensitive the network is to a human-defined concept but cannot compute its attribution in a specific prediction nor show its location within the input image. We introduce Visual-TCAV, a novel explainability framework aiming to bridge the gap between these methods by providing both local and global explanations. Visual-TCAV uses Concept Activation Vectors (CAVs) to generate class-agnostic saliency maps that show where the network recognizes a certain concept. Moreover, it can estimate the attribution of these concepts to the output of any class using a generalization of Integrated Gradients. We evaluate the method's faithfulness via a controlled experiment where the ground truth for explanations is known, showing better ground truth alignment than TCAV.
For more information, please refer to the paper available here.
If you find our work useful, please cite:
@article{
desantis2026visualtcav,
title={Visual-{TCAV}: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification},
author={Antonio De Santis and Riccardo Campi and Matteo Bianchi and Marco Brambilla},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2026},
url={https://openreview.net/forum?id=SLh00W5rhu},
note={}
}