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20 April 2024 |
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Influence Selection for Active Learning | | Zhuoming Liu
; Hao Ding
; Huaping Zhong
; Weijia Li
; Jifeng Dai
; Conghui He
; | | Date: |
20 Aug 2021 | | Abstract: | The existing active learning methods select the samples by evaluating the
sample’s uncertainty or its effect on the diversity of labeled datasets based
on different task-specific or model-specific criteria. In this paper, we
propose the Influence Selection for Active Learning(ISAL) which selects the
unlabeled samples that can provide the most positive Influence on model
performance. To obtain the Influence of the unlabeled sample in the active
learning scenario, we design the Untrained Unlabeled sample Influence
Calculation(UUIC) to estimate the unlabeled sample’s expected gradient with
which we calculate its Influence. To prove the effectiveness of UUIC, we
provide both theoretical and experimental analyses. Since the UUIC just depends
on the model gradients, which can be obtained easily from any neural network,
our active learning algorithm is task-agnostic and model-agnostic. ISAL
achieves state-of-the-art performance in different active learning settings for
different tasks with different datasets. Compared with previous methods, our
method decreases the annotation cost at least by 12%, 13% and 16% on CIFAR10,
VOC2012 and COCO, respectively. | | Source: | arXiv, 2108.09331 | | Services: | Forum | Review | PDF | Favorites | |
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