[62] Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, and Bo Han. Learning to augment distributions for out-of-distribution detection. In NeurIPS, 2023.
[63] Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, and Bo Han. Watermarking for out-of-distribution detection. In NeurIPS, 2022.
[64] Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, and Bo Han. Out-of-distribution detection with implicit outlier transformation. In ICLR, 2023.
[65] Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, et al. Usb: A unified semi-supervised learning benchmark for classification. In NeurlPS, volume 35, pages 3938-3961, 2022.
[66] Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Zhen Wu, and Jindong Wang. Freematch: Self-adaptive thresholding for semi-supervised learning. In ICLR, 2023.
[67] Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, and Tongliang Liu. Mosaic representation learning for self-supervised visual pre-training. In ICLR, 2022.
[68] Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, and Tongliang Liu. Exploring set similarity for dense self-supervised representation learning. In CVPR, pages 16590-16599, 2022.
[69] Dongxian Wu, Shu-Tao Xia, and Yisen Wang. Adversarial weight perturbation helps robust generalization. In NeurIPS, volume 33, pages 2958-2969, 2020.
[70] Yuhao Wu, Xiaobo Xia, Jun Yu, Bo Han, Gang Niu, Masashi Sugiyama, and Tongliang Liu. Making binary classification from multiple unlabeled datasets almost free of supervision. arXiv preprint arXiv:2306.07036, 2023.
[71] Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, and Tongliang Liu. Conditional consistency regularization for semi-supervised multi-label image classification. IEEE Transactions on Multimedia, 2023.
[72] Xiaobo Xia, Bo Han, Yibing Zhan, Jun Yu, Mingming Gong, Chen Gong, and Tongliang Liu. Combating noisy labels with sample selection by mining high-discrepancy examples. In ICCV, pages 1833-1843, 2023.
[73] Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, and Masashi Sugiyama. Sample selection with uncertainty of losses for learning with noisy labels. arXiv preprint arXiv:2106.00445, 2021.
[74] Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. Unsupervised data augmentation for consistency training. In NeurIPS, 2020.
[75] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. Self-training with noisy student improves imagenet classification. In CVPR, pages 10687-10698, 2020.
[76] Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin. Dash: Semi-supervised learning with dynamic thresholding. In ICML, pages 11525-11536. PMLR, 2021.
[77] Suqin Yuan, Lei Feng, and Tongliang Liu. Late stopping: Avoiding confidently learning from mislabeled examples. In ICCV, pages 16079-16088, 2023.
[78] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016.
[79] Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. In NeurIPS, volume 34, pages 18408-18419, 2021.
[80] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, 2017.