@inproceedings{f77937c48b434a9ba644bcfa05fa77ff,
title = "HAR: Hardness Aware Reweighting for Imbalanced Datasets",
abstract = "Class imbalance is a significant i ssue t hat causes neural networks to underfit t o t he r are c lasses. Traditional mitigation strategies include loss reshaping and data resampling which amount to increasing the loss contribution of minority classes and decreasing the loss contributed by the majority ones. However, by treating each example within a class equally, these methods lead to undesirable scenarios where hard-to-classify examples from the majority classes are down-weighted and easy-to-classify examples from the minority classes are up-weighted. We propose the Hardness Aware Reweighting (HAR) framework, which circumvents this issue by increasing the loss contribution of hard examples from both the majority and minority classes. This is achieved by augmenting a neural network with intermediate classifier b ranches t o e nable e arly-exiting d uring t raining. Experimental results on large-scale datasets demonstrate that HAR consistently improves state-of-the-art accuracy while saving up to 20% of inference FLOPS.",
keywords = "Class imbalance, hardness, neural networks, reweighting",
author = "Rahul Duggal and Scott Freitas and Sunny Dhamnani and Chau, {Duen Horng} and Jimeng Sun",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9671807",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "735--745",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
address = "United States",
}