  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.
