Uncertainty-guided Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a classifier
to an unlabelled target data set by only using a pre-trained source
model. However, the absence of the source data and the domain shift
makes the predictions on the target data unreliable. We propose quantifying
the uncertainty in the source model predictions and utilizing it to
guide the target adaptation. For this, we construct a probabilistic source
model by incorporating priors on the network parameters inducing a distribution
over the model predictions. Uncertainties are estimated by employing
a Laplace approximation and incorporated to identify target data
points that do not lie in the source manifold and to down-weight them
when maximizing the mutual information on the target data. Unlike recent
works, our probabilistic treatment is computationally lightweight,
decouples source training and target adaptation, and requires no specialized
source training or changes of the model architecture. We show the
advantages of uncertainty-guided SFDA over traditional SFDA in the
closed-set and open-set settings and provide empirical evidence that our
approach is more robust to strong domain shifts even without tuning.