Improving Imbalanced Learning by Pre-finetuning with Data Augmentation
Yiwen Shi, Taha ValizadehAslani, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang
Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 183:68-82, 2022.
Cite this Paper
BibTeX
@InProceedings{pmlr-v183-shi22a,
title = {Improving Imbalanced Learning by Pre-finetuning with Data Augmentation},
author = {Shi, Yiwen and ValizadehAslani, Taha and Wang, Jing and Ren, Ping and Zhang, Yi and Hu, Meng and Zhao, Liang and Liang, Hualou},
booktitle = {Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications},
pages = {68--82},
year = {2022},
editor = {Moniz, Nuno and Branco, Paula and Torgo, Luís and Japkowicz, Nathalie and Wozniak, Michal and Wang, Shuo},
volume = {183},
series = {Proceedings of Machine Learning Research},
month = {23 Sep},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v183/shi22a/shi22a.pdf},
url = {https://proceedings.mlr.press/v183/shi22a.html}
}
Endnote
%0 Conference Paper
%T Improving Imbalanced Learning by Pre-finetuning with Data Augmentation
%A Yiwen Shi
%A Taha ValizadehAslani
%A Jing Wang
%A Ping Ren
%A Yi Zhang
%A Meng Hu
%A Liang Zhao
%A Hualou Liang
%B Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications
%C Proceedings of Machine Learning Research
%D 2022
%E Nuno Moniz
%E Paula Branco
%E Luís Torgo
%E Nathalie Japkowicz
%E Michal Wozniak
%E Shuo Wang
%F pmlr-v183-shi22a
%I PMLR
%P 68--82
%U https://proceedings.mlr.press/v183/shi22a.html
%V 183
APA
Shi, Y., ValizadehAslani, T., Wang, J., Ren, P., Zhang, Y., Hu, M., Zhao, L. & Liang, H.. (2022). Improving Imbalanced Learning by Pre-finetuning with Data Augmentation. Proceedings of the Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 183:68-82 Available from https://proceedings.mlr.press/v183/shi22a.html.
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Proceedings of Machine Learning Research