An analysis of over-sampling labeled data in semi-supervised learning with FixMatch

Authors

  • Miquel Marti KTH Royal Institute of Technology, Univrses
  • Sebastian Bujwid
  • Alessandro Pieropan
  • Hossein Azizpour
  • Atsuto Maki

DOI:

https://doi.org/10.7557/18.6269

Keywords:

semi-supervised learning, mini-batch sampling

Abstract

Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. We compare it to an alternative setting where each minibatch is uniformly sampled from all the training data, labeled or not, which greatly reduces direct supervision from true labels in typical low-label regimes. However, this simpler setting can also be seen as more general and even necessary in multitask problems where over-sampling labeled data would become intractable. Our experiments on semi-supervised CIFAR-10 image classification using FixMatch show a performance drop when using the uniform sampling approach which diminishes when the amount of labeled data or the training time increases. Further, we analyse the training dynamics to understand how over-sampling of labeled data compares to uniform sampling. Our main finding is that over-sampling is especially beneficial early in training but gets less important in the later stages when more pseudo-labels become correct. Nevertheless, we also find that keeping some true labels remains important to avoid the accumulation of confirmation errors from incorrect pseudo-labels.

References

E. Arazo, D. Ortego, P. Albert, N. E. O’Connor, and K. McGuinness. Pseudolabeling and confirmation bias in deep semisupervised learning. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2020. doi: 10.1109/ IJCNN48605.2020.9207304.

P. Bachman, O. Alsharif, and D. Precup. Learning with pseudo-ensembles. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pages 3365–3373, 2014. URL https://proceedings.neurips.cc/paper/2014/hash/66be31e4c40d676991f2405aaecc6934-Abstract.html.

D. Berthelot, N. Carlini, I. J. Goodfellow, N. Papernot, A. Oliver, and C. Raffel. Mixmatch: A holistic approach to semi-supervised learning. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alch´e-Buc, E. B. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 5050–5060, 2019. URL https://proceedings.neurips.cc/paper/2019/hash/1cd138d0499a68f4bb72bee04bbec2d7-Abstract.html.

E. D. Cubuk, B. Zoph, J. Shlens, and Q. Le. Randaugment: Practical automated data augmentation with a reduced search space. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/d85b63ef0ccb114d0a3bb7b7d808028f-Abstract.html.

CuriousAI. Curiousai/mean-teacher: A state-of-the-art semi-supervised method for image recognition. URL https: //github.com/CuriousAI/mean-teacher/blob/51d20acd65/README.md#tips-for-choosing-hyperparameters-and-other-tuning.

Y. Grandvalet and Y. Bengio. Semi-supervised learning by entropy minimization. In Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada], pages 529–536, 2004. URL https://proceedings.neurips.cc/paper/2004/hash/96f2b50b5d3613adf9c27049b2a888c7-Abstract.html.

A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, 2009. URL https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf.

S. Laine and T. Aila. Temporal ensembling for semi-supervised learning. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. URL https://openreview.net/forum?id=BJ6oOfqge.

D.-H. Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, volume 3-2, page 896, 2013. URL https://storage.googleapis.com/kaggle-forum-message-attachments/8924/pseudo_label_final.pdf.

I. Loshchilov and F. Hutter. SGDR: stochastic gradient descent with warm restarts. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. URL https: //openreview.net/forum?id=Skq89Scxx.

T. Miyato, S. I. Maeda, M. Koyama, and S. Ishii. Virtual adversarial training: A regularization method for supervised and semisupervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41: 1979–1993, 8 2019. ISSN 19393539. doi: 10.1109/TPAMI.2018.2858821.

Y. E. Nesterov. A method for solving the convex programming problem with convergence rate o(1/k2). Dokl. Akad. Nauk SSSR, 269: 543–547, 1983.

K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM ’00, page 86–93, New York, NY, USA, 2000. Association for Computing Machinery. ISBN 1581133200. doi: 10.1145/354756.354805.

A. Oliver, A. Odena, C. Raffel, E. D. Cubuk, and I. J. Goodfellow. Realistic evaluation of deep semi-supervised learning algorithms. In S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr´eal, Canada, pages 3239–3250, 2018. URL https://proceedings.neurips.cc/paper/2018/hash/c1fea270c48e8079d8ddf7d06d26ab52-Abstract.html.

B. Polyak. Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5):1–17, 1964. ISSN 0041-5553. doi: 10.1016/0041-5553(64)90137-5.

A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko. Semi-supervised learning with ladder networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 3546–3554, 2015. URL https://proceedings.neurips.cc/paper/2015/hash/378a063b8fdb1db941e34f4bde584c7d-Abstract.html.

M. Sajjadi, M. Javanmardi, and T. Tasdizen. Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 1163–1171, 2016. URL https://proceedings.neurips.cc/paper/2016/hash/30ef30b64204a3088a26bc2e6ecf7602-Abstract.html.

K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. Raffel, E. D. Cubuk, A. Kurakin, and C. Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/06964dce9addb1c5cb5d6e3d9838f733-Abstract.html.

I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, volume 28 of JMLR Workshop and Conference Proceedings, pages 1139–1147. JMLR.org, 2013. URL http://proceedings.mlr.press/v28/sutskever13.html.

A. Tarvainen and H. Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 1195–1204, 2017. URL https://proceedings.neurips.cc/paper/2017/hash/68053af2923e00204c3ca7c6a3150cf7-Abstract.html.

J. E. van Engelen and H. H. Hoos. A survey on semi-supervised learning. Machine Learning, 109:373–440, 2 2020. ISSN 15730565. doi: 10.1007/s10994-019-05855-6.

Q. Xie, Z. Dai, E. H. Hovy, T. Luong, and Q. Le. Unsupervised data augmentation for consistency training. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/44feb0096faa8326192570788b38c1d1-Abstract.html.

Q. Xie, M. Luong, E. H. Hovy, and Q. V. Le. Self-training with noisy student improves imagenet classification. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 10684–10695. Computer Vision Foundation / IEEE, 2020. doi: 10.1109/CVPR42600.2020.01070.

S. Zagoruyko and N. Komodakis. Wide residual networks. In E. R. H. Richard C. Wilson and W. A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 87.1–87.12. BMVA Press, 9 2016. ISBN 1-901725-59-6. doi: 10.5244/C.30.87.

B. Zoph, G. Ghiasi, T. Lin, Y. Cui, H. Liu, E. D. Cubuk, and Q. Le. Rethinking pre-training and self-training. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/hash/27e9661e033a73a6ad8cefcde965c54d-Abstract.html.

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Published

2022-04-07