  Pseudo-label based self training approaches are a popular method for
source-free unsupervised domain adaptation. However, their efficacy depends on
the quality of the labels generated by the source trained model. These labels
may be incorrect with high confidence, rendering thresholding methods
ineffective. In order to avoid reinforcing errors caused by label noise, we
propose an uncertainty-aware mean teacher framework which implicitly filters
incorrect pseudo-labels during training. Leveraging model uncertainty allows
the mean teacher network to perform implicit filtering by down-weighing losses
corresponding uncertain pseudo-labels. Effectively, we perform automatic
soft-sampling of pseudo-labeled data while aligning predictions from the
student and teacher networks. We demonstrate our method on several domain
adaptation scenarios, from cross-dataset to cross-weather conditions, and
achieve state-of-the-art performance in these cases, on the KITTI lidar target
dataset.
