论文标题
密度反卷积和正常流量
Density Deconvolution with Normalizing Flows
论文作者
论文摘要
密度反卷积是仅在噪声浪费的样品中估算概率密度函数的任务。如果噪声正态分布，我们可以将高斯混合模型拟合到基础密度，但希望利用标准化流量的上限密度估计性能并允许任意噪声分布。由于两种调整都导致了棘手的可能性，因此我们求助于摊销的变异推断。我们证明了这种方法涉及的一些问题，但是，对实际数据的实验表明，流量已经超过了高斯混合物以进行密度反卷积。
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples. We can fit a Gaussian mixture model to the underlying density by maximum likelihood if the noise is normally distributed, but would like to exploit the superior density estimation performance of normalizing flows and allow for arbitrary noise distributions. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference. We demonstrate some problems involved in this approach, however, experiments on real data demonstrate that flows can already out-perform Gaussian mixtures for density deconvolution.
