The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Interpolation consistency training for semi-supervised learning
Open accessAbstract
We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Self-citationMomentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv · doi:10.48550/arxiv.2001.07685 · Cited by 2,892
A general framework for adversarial examples with objectives
Sharif, M., Bhagavatula, S., Bauer, L., & Reiter, M. K. (2019) · ACM Transactions on Privacy and Security · doi:10.1145/3317611 · Cited by 200
Cited by
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020) · arXiv · doi:10.48550/arxiv.2001.07685 · Cited by 2,892
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Self-Training With Noisy Student Improves ImageNet Classification
Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393
Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103678 · Cited by 549
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279
Metadata
- Authors
- Verma, V., Kawaguchi, K., Lamb, A., Kannala, J., Solin, A., Bengio, Y., & Lopez-Paz, D.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 145
- Pages
- 90-106
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2921087533
- Cited by
- 881
- References
- 7
- FWCI
- 41.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
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