[6] Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, and Xiaohua Xie. The enemy of my enemy is my friend: Exploring inverse adversaries for improving adversarial training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24678-24687, 2023.
[7] Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606, 2021.
[8] Micah Goldblum, Liam Fowl, Soheil Feizi, and Tom Goldstein. Adversarily robust distillation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3996-4003, 2020.
[9] Dong-Jun Han, Jungwuk Park, Seokil Ham, Namjin Lee, and Jaekyun Moon. Improving low-latency predictions in multi-exit neural networks via block-dependent losses. IEEE Transactions on Neural Networks and Learning Systems, 2023.
[10] Mirazul Haque, Anki Chauhan, Cong Liu, and Wei Yang. Ilfo: Adversarial attack on adaptive neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14264-14273, 2020.
[11] Sanghyun Hong, Yigitcan Kaya, Ionut-Vlad Modoranu, and Tudor Dumitras. A panda? no, it's a sloth: Slowdown attacks on adaptive multi-exit neural network inference. In International Conference on Learning Representations, 2020.
[12] Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang. Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference. In International Conference on Learning Representations, 2020.
[13] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens Van Der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. arXiv preprint arXiv:1703.09844, 2017.
[14] Hoki Kim. Torchattacks: A pytorch repository for adversarial attacks. arXiv preprint arXiv:2010.01950, 2020.
[15] Alexandros Kouris, Stylianos I Venieris, Stefanos Laskaridis, and Nicholas Lane. Multi-exit semantic segmentation networks. In European Conference on Computer Vision, pages 330-349. Springer, 2022.
[16] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[17] Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015.
[18] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[19] Sungyoon Lee, Hoki Kim, and Jaewook Lee. Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[20] Hao Li, Hong Zhang, Xiaojuan Qi, Ruigang Yang, and Gao Huang. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1891-1900, 2019.
[21] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018.
[22] Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu. Improving adversarial robustness via promoting ensemble diversity. In International Conference on Machine Learning, pages 4970-4979. PMLR, 2019.