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- W4280634096 abstract "The success of deep learning applications relies on a large number of labeled data. Active learning aims at identifying most informative unlabeled samples for labeling so as to achieve comparable performance with as few labeled data as possible. A two-stage batch-mode active learning (TBAL) method is proposed in this work. In the first stage of TBAL, we propose a new semi-supervised learning framework that offers accurate uncertainty estimation and finds a high-dimensional feature representation for unlabeled samples. In other words, it leverages a small number of labeled data as well as a large number of unlabeled data. In the second stage of TBAL, we propose a novel technique called cluster re-balancing (CRB). It takes correlations within a batch into account and fits a query strategy in batch mode. The TBAL method can obtain informative batches of samples by considering uncertainty and diversity jointly in a high-dimensional feature space. Moreover, in order to compare the robustness of semi-supervised learning based active learning methods, a new evaluation protocol based on transfer learning is introduced. Experiments on SVHN, CIFAR-10 and CUB-200-2011 datasets show that our proposed CRB based query strategy that employs a semi-supervised model with a good balance between uncertainty and diversity outperforms various baselines as well as other state-of-the-art methods by a clear margin." @default.
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- W4280634096 date "2022-08-01" @default.
- W4280634096 modified "2023-10-06" @default.
- W4280634096 title "TBAL: Two-stage batch-mode active learning for image classification" @default.
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- W4280634096 doi "https://doi.org/10.1016/j.image.2022.116731" @default.
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