In this paper, we critically evaluate Bayesian methods for uncertainty estimation in deep learning, focusing on the widely applied Laplace approximation and its variants. Our findings reveal that the conventional method of fitting the Hessian matrix negatively impacts out-of-distribution (OOD) detection efficiency. We propose a different point of view, asserting that focusing solely on optimizing prior precision can yield more accurate uncertainty estimates in OOD detection while preserving adequate calibration metrics. Moreover, we demonstrate that this property is not connected to the training stage of a model but rather to its intrinsic properties. Through extensive experimental evaluation, we establish the superiority of our simplified approach over traditional methods in the out-of-distribution domain.

翻译：在本文中，我们批判性地评估了深度学习中用于不确定性估计的贝叶斯方法，重点关注广泛应用的拉普拉斯近似及其变体。我们的发现揭示，拟合海森矩阵的传统方法对分布外检测效率产生负面影响。我们提出一种不同的观点，主张仅关注优化先验精度即可在分布外检测中获得更准确的不确定性估计，同时保持足够的校准指标。此外，我们证明这一特性与模型的训练阶段无关，而与其固有属性相关。通过广泛的实验评估，我们确立了所提出简化方法在分布外领域相对于传统方法的优越性。