[1] Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh M Nguyen. Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4840-4849, 2019.
[2] Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization, 2020.
[3] Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Joshua B. Tenenbaum, and Boris Katz. Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. In NeurIPS, 2019.
[4] Samyadeep Basu, Philip Pope, and Soheil Feizi. Influence functions in deep learning are fragile. ArXiv, abs/2006.14651, 2021.
[5] Lucas Beyer, Olivier J. H'enaff, Alexander Kolesnikov, Xiaohua Zhai, and Aaron van den Oord. Are we done with imagenet? ArXiv, abs/2006.07159, 2020.
[6] Joy Buolamwini and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In FAT, 2018.
[7] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021.
[8] Chaofan Chen, Oscar Li, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. This looks like that: deep learning for interpretable image recognition. ArXiv, abs/1806.10574, 2019.
[9] Xinlei Chen*, Saining Xie*, and Kaiming He. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057, 2021.
[10] Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. *Big data*, 5 2:153–163, 2017.
[11] Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar. Explaining latent representations with a corpus of examples. In NeurIPS, 2021.
[12] Alex J. DeGrave, Joseph D. Janizek, and Su-In Lee. Ai for radiographic covid-19 detection selects shortcuts over signal. Nature Machine Intelligence, 3(7):610-619, 2021.
[13] Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan I. Moldovan, Sylvan Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic. On robustness and transferability of convolutional neural networks. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16453-16463, 2021.
[14] Thomas Fel, Ivan Felipe, Drew Linsley, and Thomas Serre. Harmonizing the object recognition strategies of deep neural networks with humans. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[15] Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W. Picard. Dissect: Disentangled simultaneous explanations via concept traversals. ArXiv, abs/2105.15164, 2022.
[16] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Lixuan Zhu, Samyak Parajuli, Mike Guo, Dawn Xiaodong Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8320-8329, 2021.
[17] Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas. Natural language descriptions of deep visual features. ArXiv, abs/2201.11114, 2022.