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.

神经网络面对与其训练集不同的陌生数据时，存在域偏移的问题，现有的解决方法包括不加监督的域适应和确定性提取等，但它们往往会导致模型的过度确定性现象，本文提出了一种增加准确性并保证模型校准的不加监督域适应算法，适用于计算资源有限的环境。