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Auto-annotation by ensemble of models is an efficient method of learning onunlabeled data. Wrong or inaccurate annotations generated by the ensemble maylead to performance degradation of the trained model. To deal with this problemwe propose filtering the auto-labeled data using a trained model that predictsthe quality of the annotation from the degree of consensus between ensemblemodels. Using semantic segmentation as an example, we show the advantage of theproposed auto-annotation filtering over training on data contaminated withinaccurate labels.  Moreover, our experimental results show that in the case of semanticsegmentation, the performance of a state-of-the-art model can be achieved bytraining it with only a fraction (30$\%$) of the original manually labeled dataset, and replacing the rest with the auto-annotated, quality filtered labels.

Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles