集成学习半监督自动标注质量预测
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
AI总结：
提出基于集成模型一致性预测标注质量的过滤方法，用于半监督自动标注，在语义分割中仅用30%人工标注数据即可达到最先进性能。
摘要：
通过模型集成进行自动标注是在未标注数据上学习的一种高效方法。集成生成的错误或不准确标注可能导致训练模型性能下降。为解决此问题，我们提出使用一个训练好的模型来过滤自动标注数据，该模型根据集成模型之间的一致性程度预测标注质量。以语义分割为例，我们展示了所提出的自动标注过滤方法相较于在含有不准确标签的数据上训练的优势。此外，我们的实验结果表明，在语义分割的情况下，仅使用原始人工标注数据集的30%进行训练，并用经过质量过滤的自动标注标签替换其余部分，即可达到最先进模型的性能。
英文摘要：
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degradation of the trained model. To deal with this problem we propose filtering the auto-labeled data using a trained model that predicts the quality of the annotation from the degree of consensus between ensemble models. Using semantic segmentation as an example, we show the advantage of the proposed auto-annotation filtering over training on data contaminated with inaccurate labels. Moreover, our experimental results show that in the case of semantic segmentation, the performance of a state-of-the-art model can be achieved by training it with only a fraction (30$\%$) of the original manually labeled data set, and replacing the rest with the auto-annotated, quality filtered labels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
