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
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Self-citationMomentum Contrast for Unsupervised Visual Representation Learning
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Context Encoders: Feature Learning by Inpainting
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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
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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
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A Survey on Semi-, Self- and Unsupervised Learning for Image Classification
Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246
Sheu, R.-K., & Pardeshi, M. S. (2022) · Sensors · doi:10.3390/s22208068 · Cited by 129
FedHAR: Semi-Supervised Online Learning for Personalized Federated Human Activity Recognition
Yu, H., Chen, Z., Zhang, X., Chen, X., Zhuang, F., Xiong, H., & Cheng, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3136853 · Cited by 113
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
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Interpolation-based Semi-supervised Learning for Object Detection
Jeong, J., Verma, V., Hyun, M., Kannala, J., & Kwak, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01143 · Cited by 66
Self-citationMake Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
Liu, Y., Yuan, Z., Mao, H., Liang, Z., Yang, W., Qiu, Y., Cheng, T., Li, X., Xu, H., & Gao, K. (2022) · Proceedings of the 2022 International Conference on Multimodal Interaction · doi:10.1145/3536221.3556630 · Cited by 64
Qiu, Y., Lu, H., Mei, J., Bao, S., & Xu, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124734 · Cited by 5
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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