Establishing rigorous testing and certification techniques is essential before deploying new technologies like Deep Learning (DL) in safety-critical applications. We propose a testing approach that could identify weaknesses in DL models.
A surrogate model-based explainability approach is proposed for point cloud classifiers together with two different evaluation metrics validating the plausibility of the approach. The toolkit is now available online.
