1. Yutong Bai, Jieru Mei, Alan L Yuille, and Cihang Xie. 2021. Are Transformers more robust than CNNs?. In Advances in Neural Information Processing Systems, M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 26831--26843.
2. Philipp Benz, Soomin Ham, Chaoning Zhang, Adil Karjauv, and In So Kweon. 2021. Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs. British Machine Vision Conference (BMVC) (2021).
3. Understanding Robustness of Transformers for Image Classification
4. Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
5. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https://openreview.net/forum?id=YicbFdNTTy