As part of autonomous car driving systems, semantic segmentation is an
essential component to obtain a full understanding of the car's environment.
One difficulty, that occurs while training neural networks for this purpose, is
class imbalance of training data. Consequently, a neural network trained on
unbalanced data in combination with maximum a-posteriori classification may
easily ignore classes that are rare in terms of their frequency in the dataset.
However, these classes are often of highest interest. We approach such
potential misclassifications by weighting the posterior class probabilities
with the prior class probabilities which in our case are the inverse
frequencies of the corresponding classes in the training dataset. More
precisely, we adopt a localized method by computing the priors pixel-wise such
that the impact can be analyzed at pixel level as well. In our experiments, we
train one network from scratch using a proprietary dataset containing 20,000
annotated frames of video sequences recorded from street scenes. The evaluation
on our test set shows an increase of average recall with regard to instances of
pedestrians and info signs by $25\%$ and $23.4\%$, respectively. In addition,
we significantly reduce the non-detection rate for instances of the same
classes by $61\%$ and $38\%$.

研究了如何解决语义分割中训练数据类别不平衡的问题，通过利用先验概率对后验概率进行加权，从而改善对少数类别的识别效果，实验证明其对于行人和路标的识别召回率分别提高了 25% 和 23.4%，同时非检测率显著降低了 61% 和 38%。

运用决策规则处理语义分割中的类别不平衡

Application of Decision Rules for Handling Class Imbalance in Semantic  Segmentation

This paper outlines a methodology for Bayesian multimodel uncertainty
quantification (UQ) and propagation and presents an investigation into the
effect of prior probabilities on the resulting uncertainties. The UQ
methodology is adapted from the information-theoretic method previously
presented by the authors (Zhang and Shields, 2018) to a fully Bayesian
construction that enables greater flexibility in quantifying uncertainty in
probability model form. Being Bayesian in nature and rooted in UQ from small
datasets, prior probabilities in both probability model form and model
parameters are shown to have a significant impact on quantified uncertainties
and, consequently, on the uncertainties propagated through a physics-based
model. These effects are specifically investigated for a simplified plate
buckling problem with uncertainties in material properties derived from a small
number of experiments using noninformative priors and priors derived from past
studies of varying appropriateness. It is illustrated that prior probabilities
can have a significant impact on multimodel UQ for small datasets and
inappropriate (but seemingly reasonable) priors may even have lingering effects
that bias probabilities even for large datasets. When applied to uncertainty
propagation, this may result in probability bounds on response quantities that
do not include the true probabilities.

研究提出了一种贝叶斯多模型不确定性量化和传播方法，并调查了先验概率对结果不确定性的影响，结果表明先验概率在多模型不确定性上具有显著影响，尤其是在小数据集上。

先验概率对由较小数据集得出的不确定概率量化和传播的影响

The effect of prior probabilities on quantification and propagation of  imprecise probabilities resulting from small datasets

After making some general remarks, I consider two examples that illustrate
the use of Bayesian Probability Theory. The first is a simple one, the
physicist's favorite "toy," that provides a forum for a discussion of the key
conceptual issue of Bayesian analysis: the assignment of prior probabilities.
The other example illustrates the use of Bayesian ideas in the real world of
experimental physics.

本文介绍了贝叶斯概率论的两个实例，分别是讨论这一分支分析中最重要的概念 —— 先验概率赋值的物理学家 “玩具”，以及展示其在实验物理领域中应用的样例。