一种用于动态多值映射估计的深度神经网络框架及其应用
A deep neural network framework for dynamic multi-valued mapping estimation and its applications
AI总结：
本文提出一种结合生成网络与分类组件的深度神经网络框架，利用离散码本建模动态多值映射，并通过联合优化码本与不确定性估计，在多种成像任务中实现高精度多解估计。
摘要：
本文研究动态多值映射的建模与估计问题。虽然大多数数学模型对给定输入提供唯一解，但现实世界应用往往缺乏确定性解。在此类场景中，估计动态多值映射对于为每个输入提供不同的合理解是必要的。本文提出一种深度神经网络框架，该框架结合了生成网络和分类组件。其目标是通过提供可靠的uncertainty measurement（不确定性度量）来建模输入与输出之间的动态多值映射。为给定输入生成多个解需要利用由有限变量组成的离散codebook（码本）。这些变量与输入一起被送入生成网络，从而产生各种可能的输出。codebook的离散性使得能够使用分类器高效估计任意给定输入下输出的条件概率分布。通过在训练过程中使用专门设计的损失函数联合优化离散codebook及其不确定性估计，实现了高精度的近似。我们提出的框架的有效性通过其在各种成像问题中的应用得到验证，使用了合成成像数据和真实成像数据。实验结果表明，我们的框架能够准确估计具有不确定性度量的动态多值映射。
英文摘要：
This paper addresses the problem of modeling and estimating dynamic multi-valued mappings. While most mathematical models provide a unique solution for a given input, real-world applications often lack deterministic solutions. In such scenarios, estimating dynamic multi-valued mappings is necessary to suggest different reasonable solutions for each input. This paper introduces a deep neural network framework incorporating a generative network and a classification component. The objective is to model the dynamic multi-valued mapping between the input and output by providing a reliable uncertainty measurement. Generating multiple solutions for a given input involves utilizing a discrete codebook comprising finite variables. These variables are fed into a generative network along with the input, producing various output possibilities. The discreteness of the codebook enables efficient estimation of the output's conditional probability distribution for any given input using a classifier. By jointly optimizing the discrete codebook and its uncertainty estimation during training using a specially designed loss function, a highly accurate approximation is achieved. The effectiveness of our proposed framework is demonstrated through its application to various imaging problems, using both synthetic and real imaging data. Experimental results show that our framework accurately estimates the dynamic multi-valued mapping with uncertainty estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
