A new active learning method based on uncertainty sampling and redundancy alleviation strategy

Active learning (AL) is a semi-supervised learning algorithm based on human-computer interaction. It aims to achieve satisfactory performance with less manual annotation costs. How to select the most informative samples from unlabeled data for manual annotation is crucial for AL. In this paper, we propose a hybrid sampling AL method FSP-Coreset, which is based on the uncertainty and diversity of unlabeled samples. Firstly, we design a new method First Second Probability (FSP) for measuring the uncertainty of unlabeled samples based on the ratio of the first largest prediction probability to the sum of the first and the second largest probability. Compared with the entropy-based method, the FSP reduces the impact of lower probability on uncertainty measurement and can select samples with significant. Secondly, we adopt the diversity-based method Coreset to alleviate the redundant samples. Through these methods, we obtain unlabeled samples with high uncertainty and low redundant. Experimental results show that our FSP-Coreset not only alleviates redundant uncertainty samples but also accelerates the convergence of Coreset. Furthermore, the performance of FSP-Coreset is superior to comparison methods. The ablation experimental results show that our FSP performs better than the entropy-based uncertainty sampling method, and the hybrid sampling method FSP-Coreset is better than the single method.