Active Learning with Selective Removing

Constructing a training dataset is essential to train a deep learning model. However, limited budgets and the high cost of data annotation present significant challenges. Active learning has emerged to resolve this issue, and it incrementally selects useful samples to be labeled and added to the training dataset. Existing works primarily focus on expanding dataset without taking into account the usefulness of the generated annotations. Since annotation is inevitably imperfect when performed by human experts, it is essential to consider the reliability of the annotations. In this paper, we propose an enhanced active learning framework with selective removing for the efficient allocation of data. The proposed method gradually obtains valuable data while filtering out uninformative data on the training dataset and model knowledge. We evaluate the proposed method for the image classification task under a constrained data budget. Experimental results demonstrate that our approach outperforms existing active learning methods.