论文标题
深度逆散射
Deep Variational Inverse Scattering
论文作者
论文摘要
反向介质散射求解器通常重建单个溶液，而无需相关的不确定性度量。对于经典迭代求解器和新兴的深度学习方法来说都是如此。但是，不良的噪音和噪音可能会使这种单一的估计不准确或误导。虽然可以使用诸如条件归一化流量的深层网络来在反问题中采样后代，但它们通常会产生低质量的样本和不确定性估计。在本文中，我们提出了基于条件归一化流的贝叶斯U-NET U-Flow，该贝叶斯U-NET会产生高质量的后验样品，并估算出身体上肉体的不确定性。我们表明，所提出的模型在后验质量方面显着优于最近的归一化流，同时在点估计中与U-NET的性能可比。
Inverse medium scattering solvers generally reconstruct a single solution without an associated measure of uncertainty. This is true both for the classical iterative solvers and for the emerging deep learning methods. But ill-posedness and noise can make this single estimate inaccurate or misleading. While deep networks such as conditional normalizing flows can be used to sample posteriors in inverse problems, they often yield low-quality samples and uncertainty estimates. In this paper, we propose U-Flow, a Bayesian U-Net based on conditional normalizing flows, which generates high-quality posterior samples and estimates physically-meaningful uncertainty. We show that the proposed model significantly outperforms the recent normalizing flows in terms of posterior sample quality while having comparable performance with the U-Net in point estimation.
