MetaMix : Improved Meta-Learning with Interpolation-based Consistency Regularization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings of ICPR 2020
Subtitle of host publication25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers
Pages407-414
ISBN (Electronic)978-1-7281-8808-9
ISBN (Print)978-1-7281-8809-6
Publication statusPublished - Jan 2021

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Title25th International Conference on Pattern Recognition (ICPR2020)
LocationVirtual
PlaceItaly
CityMilan
Period10 - 15 January 2021

Abstract

Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new ones. However, current MAML-based algorithms have limitations in forming generalizable decision boundaries. In this paper, we propose an approach called MetaMix, which generates virtual feature-target pairs within each episode to regularize the backbone models. MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks. Experiments on the mini-ImageNet, CUB, and FC100 datasets show that MetaMix improves the performance of MAML-based algorithms and achieves state-of-the-art result when integrated with MetaTransfer Learning.

Citation Format(s)

MetaMix : Improved Meta-Learning with Interpolation-based Consistency Regularization. / Chen, Yangbin; Ma, Yun; Ko, Tom et al.

Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers, 2021. p. 407-414 9413158 (Proceedings - International Conference on Pattern Recognition).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review