1.8. 参考文献

1.8.1. 主要参考文献

  • Ahfock, D. & McLachlan, G. J. (2021). Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review. arXiv:2104.04046. https://arxiv.org/abs/2104.04046

  • Mey, A. & Loog, M. (2019). Improvability through Semi-Supervised Learning: A Survey of Theoretical Results. arXiv:1908.09574. https://arxiv.org/abs/1908.09574

  • Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.

  • Dempster, A. P., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1-22.

  • McLachlan, G. J. (1975). Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in mixed-up populations. Journal of the American Statistical Association, 70(350), 414-419.

1.8.2. 经典 SSL 相关工作

  • Blum, A. & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. COLT '98.

  • Joachims, T. (1999). Transductive inference for text classification using support vector machines. ICML '99.

  • Zhu, X., Ghahramani, Z. & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. ICML '03.

  • Belkin, M., Niyogi, P. & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399-2434.

  • Grandvalet, Y. & Bengio, Y. (2005). Semi-supervised learning by entropy minimization. NeurIPS '04.

  • Balcan, M.-F. & Blum, A. (2010). A discriminative model for semi-supervised learning. Journal of the ACM, 57(3), 1-46.

1.8.3. 深度 SSL 相关工作

  • Laine, S. & Aila, T. (2016). Temporal ensembling for semi-supervised learning. ICLR '17.

  • Tarvainen, A. & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. NeurIPS '17.

  • Miyato, T. et al. (2018). Virtual adversarial training: A regularization method for supervised and semi-supervised learning. IEEE TPAMI, 41(8).

  • Xie, Q. et al. (2019). Unsupervised data augmentation for consistency training. NeurIPS '20.

  • Sohn, K. et al. (2020). FixMatch: Simplifying semi-supervised learning with consistency and confidence. NeurIPS '20.

  • Chen, T. et al. (2020). A simple framework for contrastive learning of visual representations. ICML '20.

1.8.4. 更多资源

1.8.4.1. 综述论文

  • Van Engelen, J. E. & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373-440.

  • Yang, X. et al. (2021). A survey on deep semi-supervised learning. arXiv:2103.00550.

1.8.4.2. 书籍

  • Chapelle, O., Schölkopf, B. & Zien, A. (2006). Semi-Supervised Learning. MIT Press.

  • Zhu, X. & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning.

1.8.4.3. 开源库

  • FixMatch — Google Research

  • TorchSSL — PyTorch SSL 工具包

  • USB — Microsoft 的统一 SSL 基准

备注

本文档主要基于 Ahfock & McLachlan (2021) 和 Mey & Loog (2019) 两篇综述论文的框架进行组织。深度 SSL 部分参考了 Yang et al. (2021) 的综述以及各方法原文。