Current deep learning-based solutions for image analysis tasks are commonly
incapable of handling problems to which multiple different plausible solutions
exist. In response, posterior-based methods such as conditional Diffusion
Models and Invertible Neural Networks have emerged; however, their translation
is hampered by a lack of research on adequate validation. In other words, the
way progress is measured often does not reflect the needs of the driving
practical application. Closing this gap in the literature, we present the first
systematic framework for the application-driven validation of posterior-based
methods in inverse problems. As a methodological novelty, it adopts key
principles from the field of object detection validation, which has a long
history of addressing the question of how to locate and match multiple object
instances in an image. Treating modes as instances enables us to perform
mode-centric validation, using well-interpretable metrics from the application
perspective. We demonstrate the value of our framework through instantiations
for a synthetic toy example and two medical vision use cases: pose estimation
in surgery and imaging-based quantification of functional tissue parameters for
diagnostics. Our framework offers key advantages over common approaches to
posterior validation in all three examples and could thus revolutionize
performance assessment in inverse problems.

当前深度学习的解决方案普遍无法处理存在多个不同合理解决方案的问题。为了解决这个问题，出现了基于后验的方法，如条件扩散模型和可逆神经网络，但由于缺乏适当的验证研究，它们的转化受到阻碍。为了弥补文献中的这一空白，我们提出了应用驱动的后验方法验证的第一个系统性框架，该框架采用了目标检测验证领域的关键原则，目标检测验证领域长期以来一直在解决在图像中如何定位和匹配多个对象实例的问题。将模式视为实例使我们能够执行基于模式的验证，从应用角度使用易于解释的度量标准。我们通过对合成玩具示例和两个医学视觉用例（手术姿势估计和基于成像的功能组织参数量化诊断）进行实例化，展示了我们框架的价值。我们的框架在所有三个示例中相对于常见的后验验证方法具有关键优势，因此可能彻底改变反问题的性能评估。

反问题中基于应用的后验验证

Application-driven Validation of Posteriors in Inverse Problems

Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions.

本文综述和比较了最近发表在文献中的基于机器学习的高光谱图像分析方法，将方法按图像分析任务和机器学习算法类型进行组织，并提出了一种两向映射。文章全面涵盖了高光谱图像分析任务和机器学习算法，涵盖的图像分析任务包括土地覆盖分类、目标检测、反混合和物理参数估计，涵盖的机器学习算法包括高斯模型、线性回归、逻辑回归、支持向量机、混合高斯模型、稀疏线性模型、高斯混合模型、集成学习、有向图模型、无向图模型、聚类、高斯过程、狄利克雷过程和深度学习。同时，文章也讨论了高光谱图像分析领域的挑战和可能的未来方向。