[35] Chris Olah, Alexander Mordvintsev, and Ludwig Schubert. Feature visualization. Distill, 2017. doi: 10.23915/distill.00007. https://distill.pub/2017/features-visualization.
[36] Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, and Xuezhi Wang. Understanding and improving robustness of vision transformers through patch-based negative augmentation. ArXiv, abs/2110.07858, 2021.
[37] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020, 2021.
[38] Alexander Robey, George J. Pappas, and Hamed Hassani. Model-based domain generalization. In NeurIPS, 2021.
[39] Amir Rosenfeld, Richard S. Zemel, and John K. Tsotsos. The elephant in the room. *ArXiv*, abs/1808.03305, 2018.
[40] Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. ArXiv, abs/1911.08731, 2019.
[41] Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=ryxGuJrFvS.
[42] Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. Distributionally robust neural networks. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=ryxGuJrFvS.
[43] Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry. Do adversarially robust imagenet models transfer better? In ArXiv preprint arXiv:2007.08489, 2020.
[44] Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry. Image synthesis with a single (robust) classifier, 2019.
[45] Rakshith Shetty, Bernt Schiele, and Mario Fritz. Not using the car to see the sidewalk — quantifying and controlling the effects of context in classification and segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8210-8218, 2019.
[46] Sahil Singla and Soheil Feizi. Salient imagenet: How to discover spurious features in deep learning? In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=XVPqLyNxSyh.
[47] Sahil Singla, Besmira Nushi, S. Shah, Ece Kamar, and Eric Horvitz. Understanding failures of deep networks via robust feature extraction. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12848-12857, 2021.
[48] Nimit Sharad Sohoni, Jared A. Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré. No subclass left behind: Fine-grained robustness in coarse-grained classification problems. ArXiv, abs/2011.12945, 2020.
[49] Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems, pages 10506-10518, 2019.
[50] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed Huai hsin Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large language models. ArXiv, abs/2206.07682, 2022.
[51] Ross Wightman. Pytorch image models. https://github.com/rwrightman/pytorch-image-models, 2019.