We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning.
Stochastic Weight Averaging (SWA), which computes the first moment of
stochastic gradient descent (SGD) iterates with a modified learning rate
schedule, has recently been shown to improve generalization in deep learning.
With SWAG, we fit a Gaussian using the SWA solution as the first moment and a
low rank plus diagonal covariance also derived from the SGD iterates, forming
an approximate posterior distribution over neural network weights; we then
sample from this Gaussian distribution to perform Bayesian model averaging. We
empirically find that SWAG approximates the shape of the true posterior, in
accordance with results describing the stationary distribution of SGD iterates.
Moreover, we demonstrate that SWAG performs well on a wide variety of tasks,
including out of sample detection, calibration, and transfer learning, in
comparison to many popular alternatives including MC dropout, KFAC Laplace,
SGLD, and temperature scaling.

SWA-Gaussian 是一种用于深度学习中的不确定性表示和校准的简单、可伸缩和通用方法，它利用随机加权平均（Stochastic Weight Averaging，SWA）和高斯分布来进行贝叶斯模型平均，具有较好的拟合效果和性能。