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.

翻译：当神经网络面对与其训练集存在偏差的陌生数据时，这标志着域偏移的发生。尽管这些网络会对输入数据进行预测，但它们通常无法反映其对新颖观测数据的熟悉程度。在资源受限环境（如嵌入式系统或边缘设备）中，这一挑战尤为突出。为应对此类问题，我们旨在根据神经网络对观测数据的认知程度重新校准其决策边界，并由此提出一种名为“确定性蒸馏”的方法。现有研究虽以降低模型熵为目标进行无监督域适应，却无意中催生了校准误差困扰的模型——这一困境我们称之为“过度确定性现象”。本文深入剖析了这种传统学习模式的缺陷，并提出一种既能提升精度又能保证模型校准、同时适用于有限计算资源环境的UDA算法。