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Digital data collected over the decades and data currently being producedwith use of information technology is vastly the unlabeled data or data withoutdescription. The unlabeled data is relatively easy to acquire but expensive tolabel even with use of domain experts. Most of the recent works focus on use ofactive learning with uncertainty metrics measure to address this problem.Although most uncertainty selection strategies are very effective, they fail totake informativeness of the unlabeled instances into account and are prone toquerying outliers. In order to address these challenges we propose an hybridapproach of computing both the uncertainty and informativeness of an instance,then automaticaly label the computed instances using budget annotator. Toreduce the annotation cost, we employ the state-of-the-art pre-trained modelsin order to avoid querying information already contained in those models. Ourextensive experiments on different sets of datasets demonstrate the efficacy ofthe proposed approach.

Deep Active Learning with Budget Annotation