  Unsupervised source-free domain adaptation methods aim to train a model for
the target domain utilizing a pretrained source-domain model and unlabeled
target-domain data, particularly when accessibility to source data is
restricted due to intellectual property or privacy concerns. Traditional
methods usually use self-training with pseudo-labeling, which is often
subjected to thresholding based on prediction confidence. However, such
thresholding limits the effectiveness of self-training due to insufficient
supervision. This issue becomes more severe in a source-free setting, where
supervision comes solely from the predictions of the pre-trained source model.
In this study, we propose a novel approach by incorporating a mean-teacher
model, wherein the student network is trained using all predictions from the
teacher network. Instead of employing thresholding on predictions, we introduce
a method to weight the gradients calculated from pseudo-labels based on the
reliability of the teacher's predictions. To assess reliability, we introduce a
novel approach using proxy-based metric learning. Our method is evaluated in
synthetic-to-real and cross-city scenarios, demonstrating superior performance
compared to existing state-of-the-art methods.
