@inproceedings{3addd8a8d5ee4ae8a14bdb49ad44614a,
title = "A Two-Stage Active Learning Method for Image Classification",
abstract = "Recent successes in learning-based image classification rely heavily on a large number of annotated training samples, which often require considerable human effort. In this paper, we propose a new two-stage active learning (AL) method for image classification with a query strategy considering both uncertainty and diversity. In the first stage, the uncertainty is used to determine the candidate set. In the second stage, the candidate set is clustered, and the nearest sample from the cluster center is selected to increase the diversity of samples. Our method uses Poly-1 loss as the classification loss and Binary Cross Entropy (BCE) as the binary loss to distinguish between the labeled samples and the unlabeled samples. We train the two classifiers in a joint way and evaluate our method on the CIFAR10 and CIFAR100 datasets. The rich experimental results show that the proposed method outperforms the state-of-the-art AL methods in image classification tasks.",
keywords = "active learning, image classification, joint learning, query strategy",
author = "Feiyue Wang and Xu Li and Yifan Zhang and Baoguo Wei and Lixin Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",
year = "2022",
doi = "10.1109/ICIEA54703.2022.10005962",
language = "英语",
series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1134--1139",
editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",
booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
}