Overview
Explainable AI (XAI) seeks to tackle the opacity of deep neural network decisions. Moving beyond the conventional focus on 2D imagery ([1],[2]), our research provides the first method to provide Counterfactual Explanations (CEs) for 3D point cloud classifiers. Specifically, we propose for the first time a method for creating counterfactuals with explicit semantic guidance.
Objectives
- Enhancing the performance of the semantic guidance by developing a new optimization algorithm.
- Extent the semantic guidance to a wider range of counterfactual generators.
Prerequisite
- Python + PyTorch proficiency
- Experience with Kubernetes + Docker
- Knowledge of deep learning 3D generation pipelines (VAE, GANs, Diffusion, etc), especially for point clouds data.
Contact
References
[1] Rodriguez, P., Caccia, M., Lacoste, A., Zamparo, L., Laradji, I., Charlin, L., & Vazquez, D. (2021). Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations_. arXiv preprint arXiv:2103.10226_.
[2] Mehdi Zemni, Mickaël Chen, Έloi Zablocki, Hedi Ben-Younes, Patrick Pérez, & Matthieu Cord (2023). OCTET: Object-aware Counterfactual Explanations. In _IEEE Conference on Computer Vision and Pattern Recognition, CVPR.