[24] Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur. Sharpness-aware minimization for efficiently improving generalization. In ICLR, 2020.
[25] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, pages 2672–2680, 2014.
[26] Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015.
[27] Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In NeurIPS, pages 529-536, 2005.
[28] Lan-Zhe Guo and Yu-Feng Li. Class-imbalanced semi-supervised learning with adaptive thresholding. In ICML, pages 8082-8094. PMLR, 2022.
[29] Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, and Zhi-Hua Zhou. Safe deep semi-supervised learning for unseen-class unlabeled data. In ICML, 2020.
[30] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020.
[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[32] Rundong He, Zhongyi Han, Yang Yang, and Yilong Yin. Not all parameters should be treated equally: Deep safe semi-supervised learning under class distribution mismatch. In AAAI, pages 6874-6883. AAAI Press, 2022.
[33] Rundong He, Zhongyi Han, and Yilong Yin. Towards safe and robust weakly-supervised anomaly detection under subpopulation shift. Knowledge-Based Systems, 250:109088, 2022.
[34] Ziming Hong, Shiming Chen, Guo-Sen Xie, Wenhan Yang, Jian Zhao, Yuanjie Shao, Qinmu Peng, and Xinge You. Semantic compression embedding for generative zero-shot learning. In IJCAI, volume 7, pages 956-963, 2022.
[35] Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, and Tongliang Liu. Puddles: Phase-amplitude spectrum disentangled early stopping for learning with noisy labels. In ICCV, pages 16719-16730, 2023.
[36] Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, and Tongliang Liu. Harnessing out-of-distribution examples via augmenting content and style. In ICLR, 2023.
[37] Zhuo Huang, Chao Xue, Bo Han, Jian Yang, and Chen Gong. Universal semi-supervised learning. In NeurIPS, volume 34, 2021.
[38] Zhuo Huang, Jian Yang, and Chen Gong. They are not completely useless: Towards recycling transferable unlabeled data for class-mismatched semi-supervised learning. IEEE Transactions on Multimedia, 2022.
[39] Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407, 2018.
[40] Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, and Tongliang Liu. Unleashing the potential of regularization strategies in learning with noisy labels. arXiv preprint arXiv:2307.05025, 2023.
[41] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR, 2017.
[42] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.