# Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles Source: [Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:27:53.383Z Authors: Dror Simon, Miriam Farber, Roman Goldenberg Published: 2019-10-30T17:10:21.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()