Unsupervised Fusion Feature Matching for Data Bias in Uncertainty Active Learning

Active learning (AL) aims to sample the most valuable data for model improvement from the unlabeled pool. Traditional works, especially uncertainty-based methods, are prone to suffer from a data bias issue, which means that selected data cannot cover the entire unlabeled pool well. Although there have been lots of literature works focusing on this issue recently, they mainly benefit from the huge additional training costs and the artificially designed complex loss. The latter causes these methods to be redesigned when facing new models or tasks, which is very time-consuming and laborious. This article proposes a feature-matching-based uncertainty that resamples selected uncertainty data by feature matching, thus removing similar data to alleviate the data bias issue. To ensure that our proposed method does not introduce a lot of additional costs, we specially design a unsupervised fusion feature matching (UFFM), which does not require any training in our novel AL framework. Besides, we also redesign several classic uncertainty methods to be applied to more complex visual tasks. We conduct rigorous experiments on lots of standard benchmark datasets to validate our work. The experimental results show that our UFFM is better than the similar unsupervised feature matching technologies, and our proposed uncertainty calculation method outperforms random sampling, classic uncertainty approaches, and recent state-of-the-art (SOTA) uncertainty approaches.