Deep Active Learning with Budget Annotation
- URL: http://arxiv.org/abs/2208.00508v1
- Date: Sun, 31 Jul 2022 20:20:44 GMT
- Title: Deep Active Learning with Budget Annotation
- Authors: Kinyua Gikunda
- Abstract summary: We propose a hybrid approach of computing both the uncertainty and informativeness of an instance.
We employ the state-of-the-art pre-trained models in order to avoid querying information already contained in those models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital data collected over the decades and data currently being produced
with use of information technology is vastly the unlabeled data or data without
description. The unlabeled data is relatively easy to acquire but expensive to
label even with use of domain experts. Most of the recent works focus on use of
active learning with uncertainty metrics measure to address this problem.
Although most uncertainty selection strategies are very effective, they fail to
take informativeness of the unlabeled instances into account and are prone to
querying outliers. In order to address these challenges we propose an hybrid
approach of computing both the uncertainty and informativeness of an instance,
then automaticaly label the computed instances using budget annotator. To
reduce the annotation cost, we employ the state-of-the-art pre-trained models
in order to avoid querying information already contained in those models. Our
extensive experiments on different sets of datasets demonstrate the efficacy of
the proposed approach.
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