  The mid-infrared spectra of galaxies are rich in features such as the
Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give
valuable information about the physics of galaxies and their evolution. For
example they can provide information about the relative contribution of star
formation and accretion from a supermassive black hole to the power output of
galaxies. However, the mid-infrared spectra are currently available for a very
small fraction of galaxies that have been detected in deep multi-wavelength
surveys of the sky. In this paper we explore whether Deep Generative Network
methods can be used to reconstruct mid-infrared spectra in the 5-35{\mu}m range
using the limited multi-wavelength photometry in ~20 bands from the ultraviolet
to the submillimeter which is typically available in extragalactic surveys. For
this purpose we use simulated spectra computed with a combination of radiative
transfer models for starbursts, active galactic nucleus (AGN) tori and host
galaxies. We find that our method using Deep Generative Networks, namely
Generative Adversarial Networks and Generative Latent Optimization models, can
efficiently produce high quality reconstructions of mid-infrared spectra in
~70% of the cases.
