Neural Radiance Fields have achieved remarkable results for novel view
synthesis but still lack a crucial component: precise measurement of
uncertainty in their predictions. Probabilistic NeRF methods have tried to
address this, but their output probabilities are not typically accurately
calibrated, and therefore do not capture the true confidence levels of the
model. Calibration is a particularly challenging problem in the sparse-view
setting, where additional held-out data is unavailable for fitting a calibrator
that generalizes to the test distribution. In this paper, we introduce the
first method for obtaining calibrated uncertainties from NeRF models. Our
method is based on a robust and efficient metric to calculate per-pixel
uncertainties from the predictive posterior distribution. We propose two
techniques that eliminate the need for held-out data. The first, based on patch
sampling, involves training two NeRF models for each scene. The second is a
novel meta-calibrator that only requires the training of one NeRF model. Our
proposed approach for obtaining calibrated uncertainties achieves
state-of-the-art uncertainty in the sparse-view setting while maintaining image
quality. We further demonstrate our method's effectiveness in applications such
as view enhancement and next-best view selection.

通过引入一种基于鲁棒而高效的指标的方法，从预测后验分布中计算每个像素的不确定性；我们提出了两种消除需保留数据需求的技术，并提出了一种新颖的元校准器，只需要训练一个 NeRF 模型。从而在稀疏视图设置中获得了最新的不确定性，同时保持图像质量。我们在视图增强和最佳视图选择等应用中展示了我们方法的有效性。

神经辐射场的校准不确定性

Calibrated Uncertainties for Neural Radiance Fields

Active learning algorithms have become increasingly popular for training
models with limited data. However, selecting data for annotation remains a
challenging problem due to the limited information available on unseen data. To
address this issue, we propose EdgeAL, which utilizes the edge information of
unseen images as {\it a priori} information for measuring uncertainty. The
uncertainty is quantified by analyzing the divergence and entropy in model
predictions across edges. This measure is then used to select superpixels for
annotation. We demonstrate the effectiveness of EdgeAL on multi-class Optical
Coherence Tomography (OCT) segmentation tasks, where we achieved a 99% dice
score while reducing the annotation label cost to 12%, 2.3%, and 3%,
respectively, on three publicly available datasets (Duke, AROI, and UMN). The
source code is available at https://github.com/Mak-Ta-Reque/EdgeAL

利用未见数据的边缘信息作为先验信息进行不确定度测量，提出了 EdgeAL 主动学习算法，并在多类别光学相干断层扫描（OCT）分割任务中取得了 99% 的 Dice 分数，同时将注释标签成本分别降低了 12%、2.3% 和 3%（在三个公开数据集 Duke，AROI 和 UMN 上）

EdgeAL：基于边缘估计的光学相干断层扫描的主动学习方法

EdgeAL: An Edge Estimation Based Active Learning Approach for OCT  Segmentation

The uncertainty measurement of classifiers' predictions is especially
important in applications such as medical diagnoses that need to ensure limited
human resources can focus on the most uncertain predictions returned by machine
learning models. However, few existing uncertainty models attempt to improve
overall prediction accuracy where human resources are involved in the text
classification task. In this paper, we propose a novel neural-network-based
model that applies a new dropout-entropy method for uncertainty measurement. We
also design a metric learning method on feature representations, which can
boost the performance of dropout-based uncertainty methods with smaller
prediction variance in accurate prediction trials. Extensive experiments on
real-world data sets demonstrate that our method can achieve a considerable
improvement in overall prediction accuracy compared to existing approaches. In
particular, our model improved the accuracy from 0.78 to 0.92 when 30\% of the
most uncertain predictions were handed over to human experts in "20NewsGroup"
data.

本篇论文提出了一种基于神经网络和新型 dropout - 熵测量方法的模型以及基于特征表示的度量学习方法，可以更精确地预测，提高分类器的预测准确性，尤其在医学诊断等需要确定不确定预测的领域有广泛的应用。

文件分类中的不确定性缓解

Mitigating Uncertainty in Document Classification

Dropout methods are a family of stochastic techniques used in neural network
training or inference that have generated significant research interest and are
widely used in practice. They have been successfully applied in neural network
regularization, model compression, and in measuring the uncertainty of neural
network outputs. While original formulated for dense neural network layers,
recent advances have made dropout methods also applicable to convolutional and
recurrent neural network layers. This paper summarizes the history of dropout
methods, their various applications, and current areas of research interest.
Important proposed methods are described in additional detail.

本文总结了 dropout 方法的历史、各种应用以及当前研究兴趣的领域，同时详细描述了一些重要的提出方法。