[19] Giang Nguyen, Daeyoung Kim, and Anh Nguyen. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. Advances in Neural Information Processing Systems (NeurIPS), 2021.
[20] Anh Nguyen, Jason Yosinski, and Jeff Clune. Understanding neural networks via feature visualization: A survey. Explainable AI: interpreting, explaining and visualizing deep learning, pages 55-76, 2019.
[21] Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu, Philipp Weiß, and Tim Landgraf. Do users benefit from interpretable vision? a user study, baseline, and dataset. Proceedings of the International Conference on Learning Representations (ICLR), 2022.
[22] Peter Hase and Mohit Bansal. Evaluating explainable ai: Which algorithmic explanations help users predict model behavior? Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Short Papers), 2020.
[23] Leon Sixt, Maximilian Granz, and Tim Landgraf. When explanations lie: Why many modified bp attributions fail. In Proceedings of the International Conference on Machine Learning (ICML), 2020.
[24] Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Advances in Neural Information Processing Systems (NIPS), 2018.
[25] Dylan Slack, Anna Hilgard, Sameer Singh, and Himabindu Lakkaraju. Reliable post hoc explanations: Modeling uncertainty in explainability. Advances in Neural Information Processing Systems (NeurIPS), 34, 2021.
[26] Sukrut Rao, Moritz Böhle, and Bernt Schiele. Towards better understanding attribution methods. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[27] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013.
[28] Aravindh Mahendran and Andrea Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5188-5196, 2015.
[29] Anh Nguyen, Jason Yosinski, and Jeff Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 427-436, 2015.
[30] Mike Tyka. Class visualization with bilateral filters. 2016. URL: https://mtyka.github.io/deepdream/2016/02/05/bilateral-class-vis.html, 2(3), 2016.
[31] M. Øygard Audun. Visualizing googlenet classes. URL: https://www.auduno.com/2015/07/29/visualizing-googlenet-classes/, 2(3), 2015.
[32] Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Robustness may be at odds with accuracy. arXiv preprint arXiv:1805.12152, 2018.
[33] Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. Image synthesis with a single (robust) classifier. Advances in Neural Information Processing Systems, 32, 2019.
[34] Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, and Aleksander Madry. Adversarial robustness as a prior for learned representations. arXiv preprint arXiv:1906.00945, 2019.
[35] Alexander Mordvintsev, Christopher Olah, and Mike Tyka. Inceptionism: Going deeper into neural networks. https://blog.research.google/2015/06/inceptionism-going-deeper-into-neural.html?m=1, 2015.
[36] Amin Ghiasi, Hamid Kazemi, Steven Reich, Chen Zhu, Micah Goldblum, and Tom Goldstein. Plug-in inversion: Model-agnostic inversion for vision with data augmentations. Proceedings of the International Conference on Machine Learning (ICML), 2021.
[37] Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, and Tom Goldstein. What do vision transformers learn? a visual exploration. preprint, 2022.
[38] Donglai Wei, Bolei Zhou, Antonio Torrabla, and William Freeman. Understanding intra-class knowledge inside cnn. arXiv preprint arXiv:1507.02379, 2015.