Interpolation consistency training for semi-supervised learning

Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
Pages3635-3641
ISBN (Electronic)978-0-9992411-4-1
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28
https://ijcai19.org/
http://ijcai19.org/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
CountryChina
CityMacao
Period10/08/201916/08/2019
Internet address

Keywords

  • Deep Learning

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  • Cite this

    Verma, V., Lamb, A., Kannala, J., Bengio, Y., & Lopez-Paz, D. (2019). Interpolation consistency training for semi-supervised learning. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) (pp. 3635-3641) https://doi.org/10.24963/ijcai.2019/504