  The Search for Extra-terrestrial Intelligence (SETI) aims to find
technological signals of extra-solar origin. Radio frequency SETI is
characterized by large unlabeled datasets and complex interference environment.
The infinite possibilities of potential signal types require generalizable
signal processing techniques with little human supervision. We present a
generative model of self-supervised deep learning that can be used for anomaly
detection and spatial filtering. We develop and evaluate our approach on
spectrograms containing narrowband signals collected by Breakthrough Listen at
the Green Bank telescope. The proposed approach is not meant to replace current
narrowband searches but to demonstrate the potential to generalize to other
signal types.
