[40] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6022-6031, 2019.
[41] Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin Dogus Cubuk. Improving robustness without sacrificing accuracy with patch gaussian augmentation. ArXiv, abs/1906.02611, 2019.
[42] A. F. M. Shahab Uddin, Mst Sirazam Monira, Wheemyung Shin, TaeChoong Chung, and Sung-Ho Bae. Saliencymix: A saliency guided data augmentation strategy for better regularization. ArXiv, abs/2006.01791, 2020.
[43] Jihao Liu, B. Liu, Hang Zhou, Hongsheng Li, and Yu Liu. Tokenmix: Rethinking image mixing for data augmentation in vision transformers. In European Conference on Computer Vision, 2022.
[44] Zicheng Liu, Siyuan Li, Di Wu, Zhiyuan Chen, Lirong Wu, Jianzhu Guo, and Stan Z. Li. Automix: Unveiling the power of mixup for stronger classifiers. In European Conference on Computer Vision, 2021.
[45] Samuel G. Finlayson, Isaac S. Kohane, and Andrew Beam. Adversarial attacks against medical deep learning systems. ArXiv, abs/1804.05296, 2018.
[46] Nicolas Papernot, Patrick Mcdaniel, Arunesh Sinha, and Michael P. Wellman. Sok: Security and privacy in machine learning. 2018 IEEE European Symposium on Security and Privacy (EuroS&P), pages 399-414, 2018.
[47] Ludmila Skaf, Elvira Buonocore, Stefano Dumontet, Roberto Capone, and Pier Paolo Franzese. Applying network analysis to explore the global scientific literature on food security. *Ecol. Informatics*, 56:101062, 2020.
[48] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. Language is not all you need: Aligning perception with language models. ArXiv, abs/2302.14045, 2023.
[49] OpenAI. Gpt-4 technical report. ArXiv, abs/2303.08774, 2023.
[50] Yongliang Shen, Kaitao Song, Xu Tan, Dong Sheng Li, Weiming Lu, and Yue Ting Zhuang. Huggingppt: Solving ai tasks with chatgpt and its friends in huggingface. ArXiv, abs/2303.17580, 2023.
[51] Dan Hendrycks and Thomas G. Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. ArXiv, abs/1903.12261, 2018.
[52] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. Boosting adversarial attacks with momentum. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9185-9193, 2017.
[53] Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Loddon Yuille, and Quoc V. Le. Adversarial examples improve image recognition. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 816-825, 2019.
[54] Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan Loddon Yuille, and Kaiming He. Feature denoising for improving adversarial robustness. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 501-509, 2018.
[55] Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. ArXiv, abs/1706.04599, 2017.
[56] Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. ArXiv, abs/1807.00263, 2018.
[57] Yaniv Ovadia, Emily Fertig, Jie Jessie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, and Jasper Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. ArXiv, abs/1906.02530, 2019.
[58] Debidatta Dwibedi, Ishan Misra, and Martial Hebert. Cut, paste and learn: Surprisingly easy synthesis for instance detection. 2017 IEEE International Conference on Computer Vision (ICCV), pages 1310-1319, 2017.
[59] Hirokatsu Kataoka, Asato Matsumoto, Ryosuke Yamada, Yutaka Satoh, Eisuke Yamagata, and Nakamasa Inoue. Formula-driven supervised learning with recursive tiling patterns. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pages 4081-4088, 2021.