Deep active learning in the presence of outlier examples poses a realistic yet challenging scenario. Acquiring unlabeled data for annotation requires a delicate balance between avoiding outliers to conserve the annotation budget and prioritizing useful inlier examples for effective training. In this work, we present an approach that leverages three highly synergistic components, which are identified as key ingredients: joint classifier training with inliers and outliers, semi-supervised learning through pseudo-labeling, and model ensembling. Our work demonstrates that ensembling significantly enhances the accuracy of pseudo-labeling and improves the quality of data acquisition. By enabling semi-supervision through the joint training process, where outliers are properly handled, we observe a substantial boost in classifier accuracy through the use of all available unlabeled examples. Notably, we reveal that the integration of joint training renders explicit outlier detection unnecessary; a conventional component for acquisition in prior work. The three key components align seamlessly with numerous existing approaches. Through empirical evaluations, we showcase that their combined use leads to a performance increase. Remarkably, despite its simplicity, our proposed approach outperforms all other methods in terms of performance. Code: https://github.com/vladan-stojnic/active-outliers

为了在存在异常样本的情况下进行深度主动学习，本文介绍了一种方法，该方法利用了三个高度协同的组成部分：与内点和异常点的联合分类器训练、通过伪标记进行半监督学习以及模型集成。通过联合训练过程启用半监督，我们观察到分类器准确度显著提高，并使用所有可用的未标记样本。令人惊讶的是，尽管简单，我们提出的方法在性能方面优于其他所有方法。

使用内点和外点进行半监督主动学习的训练集合