  When neural networks are confronted with unfamiliar data that deviate from
their training set, this signifies a domain shift. While these networks output
predictions on their inputs, they typically fail to account for their level of
familiarity with these novel observations. This challenge becomes even more
pronounced in resource-constrained settings, such as embedded systems or edge
devices. To address such challenges, we aim to recalibrate a neural network's
decision boundaries in relation to its cognizance of the data it observes,
introducing an approach we coin as certainty distillation. While prevailing
works navigate unsupervised domain adaptation (UDA) with the goal of curtailing
model entropy, they unintentionally birth models that grapple with calibration
inaccuracies - a dilemma we term the over-certainty phenomenon. In this paper,
we probe the drawbacks of this traditional learning model. As a solution to the
issue, we propose a UDA algorithm that not only augments accuracy but also
assures model calibration, all while maintaining suitability for environments
with limited computational resources.
