@inproceedings{d14b074d01794c5789eb14f4b70cf5b8,
title = "Regularization with multiple feature combination for few-shot learning",
abstract = "Few-shot learning solves problems with a limited amount of labeled examples. Our analysis shows the existing metric-based methods concentrate on highly discriminative features while not fully utilizing whole capacity. In this work, we propose a novel regularization technique that constrains the model to exploit whole capacity by distinguishing data with multiple feature combinations. Our approach achieves state-of the-art performance in several public benchmarks compared to the existing metric-based methods.",
keywords = "Few-shot learning, Metric-based method, Regularization",
author = "Lee, \{Su Been\} and Park, \{Jun Ho\} and Kim, \{Ji Young\} and Lee, \{Seung Yeol\} and Heo, \{Jae Pil\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021 ; Conference date: 17-01-2021 Through 20-01-2021",
year = "2021",
month = jan,
doi = "10.1109/BigComp51126.2021.00072",
language = "English",
series = "Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "334--337",
editor = "Herwig Unger and Jinho Kim and U Kang and Chakchai So-In and Junping Du and Walid Saad and Young-guk Ha and Christian Wagner and Julien Bourgeois and Chanboon Sathitwiriyawong and Hyuk-Yoon Kwon and Carson Leung",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data and Smart Computing, BigComp 2021",
}