[38] David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, et al. ReMix-Match: Semi-supervised learning with distribution matching and augmentation anchoring. In International Conference on Learning Representations, 2020.
[39] Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, et al. FreeMatch: Self-adaptive thresholding for semi-supervised learning. In International Conference on Learning Representations, 2023.
[40] Wang Feng, Kong Tao, Zhang Rufeng, and Liu Huaping. Self-supervised learning by estimating twin class distribution. IEEE Transactions on Image Processing, 2023.
[41] Elad Amrani, Leonid Karlinsky, and Alex Bronstein. Self-supervised classification network. In European Conference on Computer Vision, 2022.
[42] Jianfeng Wang, Thomas Lukasiewicz, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, and Alexandros Neophytou. NP-Match: When neural processes meet semi-supervised learning. In International Conference on Machine Learning, 2022.
[43] Bowen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, et al. FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling. In Advances in Neural Information Processing Systems, 2021.
[44] Carl Doersch, Abhinav Gupta, and Alexei A. Efros. Unsupervised visual representation learning by context prediction. In International Conference on Computer Vision, 2015.
[45] Xiaolong Wang and Abhinav Gupta. Unsupervised learning of visual representations using videos. In International Conference on Computer Vision, 2015.
[46] Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving Jigsaw puzzles. In European Conference on Computer Vision, 2016.
[47] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In European Conference on Computer Vision, 2016.
[48] Deepak Pathak, Ross Girshick, Piotr Dólar, Trevor Darrell, and Bharath Hariharan. Learning features by watching objects move. In Computer Vision and Pattern Recognition, 2017.
[49] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018.
[50] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, et al. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43-76, 2020.
[51] Kuniaki Saito, Donghyun Kim, Stan Sclaroff, and Kate Saenko. Universal domain adaptation through self supervision. In Advances in Neural Information Processing Systems, 2020.
[52] Kuniaki Saito and Kate Saenko. Ovanet: One-vs-all network for universal domain adaptation. In International Conference on Computer Vision, 2021.
[53] Gilles Blanchard, Aniket Anand Deshmukh, Ürun Dogan, Gyemin Lee, and Clayton Scott. Domain generalization by marginal transfer learning. The Journal of Machine Learning Research, 22(1):46-100, 2021.
[54] Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Kate Saenko, and Bryan A. Plummer.  $\mathrm{ERM} + +$ : An improved baseline for domain generalization. arXiv preprint arXiv:2304.01973, 2023.
[55] Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, et al. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2022.
[56] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems, 2020.
[57] OpenAI. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023.