论文标题
解决具有深层生成网络的光谱能量分布的逆问题
Solving Inverse Problems for Spectral Energy Distributions with Deep Generative Networks
论文作者
论文摘要
我们提出了一种解决一类复杂天文信号的反问题的端到端方法，即光谱能量分布（SED）。我们的目标是通过稀缺和/或不可靠的测量来重建此类信号。我们通过利用深层生成网络的形式利用博学的结构性来实现这一目标。类似的方法几乎仅针对显示有用的属性（例如局部，周期性）的图像进行了测试。但是，SED缺乏使问题更具挑战性的这种特性。我们设法使用生成的潜在优化模型成功地将方法扩展到SED，该模型训练有明显较少和损坏的数据。
We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs). Our goal is to reconstruct such signals from scarce and/or unreliable measurements. We achieve that by leveraging a learned structural prior in the form of a Deep Generative Network. Similar methods have been tested almost exclusively for images which display useful properties (e.g., locality, periodicity) that are implicitly exploited. However, SEDs lack such properties which make the problem more challenging. We manage to successfully extend the methods to SEDs using a Generative Latent Optimization model trained with significantly fewer and corrupted data.
