Neural radiance fields with stochasticity have garnered significant interest
by enabling the sampling of plausible radiance fields and quantifying
uncertainty for downstream tasks. Existing works rely on the independence
assumption of points in the radiance field or the pixels in input views to
obtain tractable forms of the probability density function. However, this
assumption inadvertently impacts performance when dealing with intricate
geometry and texture. In this work, we propose an independence-assumption-free
probabilistic neural radiance field based on Flow-GAN. By combining the
generative capability of adversarial learning and the powerful expressivity of
normalizing flow, our method explicitly models the density-radiance
distribution of the whole scene. We represent our probabilistic NeRF as a
mean-shifted probabilistic residual neural model. Our model is trained without
an explicit likelihood function, thereby avoiding the independence assumption.
Specifically, We downsample the training images with different strides and
centers to form fixed-size patches which are used to train the generator with
patch-based adversarial learning. Through extensive experiments, our method
demonstrates state-of-the-art performance by predicting lower rendering errors
and more reliable uncertainty on both synthetic and real-world datasets.

利用 Flow-GAN 提出了一种无独立性假设的概率神经辐射场模型，通过结合对抗学习的生成能力和正则化流的强大表达能力，显式地建模整个场景的密度 - 辐射分布，实现了最先进的性能，并在合成和真实数据集上预测更低的渲染误差和更可靠的不确定性。

FG-NeRF: 基于 Flow-GAN 的概率性神经辐射场用于独立假设无关的不确定性估计

FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for  Independence-Assumption-Free Uncertainty Estimation

In this article I describe a research agenda for securing machine learning
models against adversarial inputs at test time. This article does not present
results but instead shares some of my thoughts about where I think that the
field needs to go. Modern machine learning works very well on I.I.D. data: data
for which each example is drawn {\em independently} and for which the
distribution generating each example is {\em identical}. When these assumptions
are relaxed, modern machine learning can perform very poorly. When machine
learning is used in contexts where security is a concern, it is desirable to
design models that perform well even when the input is designed by a malicious
adversary. So far most research in this direction has focused on an adversary
who violates the {\em identical} assumption, and imposes some kind of
restricted worst-case distribution shift. I argue that machine learning
security researchers should also address the problem of relaxing the {\em
independence} assumption and that current strategies designed for robustness to
distribution shift will not do so. I recommend {\em dynamic models} that change
each time they are run as a potential solution path to this problem, and show
an example of a simple attack using correlated data that can be mitigated by a
simple dynamic defense. This is not intended as a real-world security measure,
but as a recommendation to explore this research direction and develop more
realistic defenses.

描述了一项关于在测试时保护机器学习模型免受对抗性输入攻击的研究议程，建议使用动态模型作为解决这个问题的潜在方案。