[1] Chris Olah, Alexander Mordvintsev, and Ludwig Schubert. Feature visualization. Distill, 2(11):e7, 2017.
[2] Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. ArXiv e-print, 2017.
[3] Alon Jacovi, Ana Marasovic, Tim Miller, and Yoav Goldberg. Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 624-635, 2021.
[4] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In Workshop Proceedings of the International Conference on Learning Representations (ICLR), 2014.
[5] Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. Smoothgrad: removing noise by adding noise. In Workshop on Visualization for Deep Learning, Proceedings of the International Conference on Machine Learning (ICML), 2017.
[6] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017.
[7] Thomas Fel, Remi Cadene, Mathieu Chalvidal, Matthieu Cord, David Vigouroux, and Thomas Serre. Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
[8] Paul Novello, Thomas Fel, and David Vigouroux. Making sense of dependence: Efficient black-box explanations using dependence measure. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[9] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the International Conference on Machine Learning (ICML), 2017.
[10] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pages 818-833. Springer, 2014.
[11] Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In Proceedings of the International Conference on Machine Learning (ICML), 2017.
[12] Ruth C. Fong and Andrea Vedaldi. Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.
[13] Mara Graziani, Iam Palatnik de Sousa, Marley MBR Vellasco, Eduardo Costa da Silva, Henning Müller, and Vincent Andrearczyk. Sharpening local interpretable model-agnostic explanations for histopathology: improved understandability and reliability. In Medical Image Computing and Computer Assisted Intervention (MICCAI). Springer, 2021.
[14] Vitali Petsiuk, Abir Das, and Kate Saenko. Rise: Randomized input sampling for explanation of black-box models. In Proceedings of the British Machine Vision Conference (BMVC), 2018.
[15] Thomas Fel, Melanie Ducoffe, David Vigouroux, Remi Cadene, Mikael Capelle, Claire Nicodeme, and Thomas Serre. Don't lie to me! robust and efficient explainability with verified perturbation analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[16] Marouane II Idrissi, Nicolas Bousquet, Fabrice Gamboa, Bertrand Iooss, and Jean-Michel Loubes. On the coalitional decomposition of parameters of interest, 2023.
[17] Julien Colin, Thomas Fel, Rémi Cadène, and Thomas Serre. What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[18] Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky. HIVE: Evaluating the human interpretability of visual explanations. In Proceedings of the IEEE European Conference on Computer Vision (ECCV), 2022.