  With the advent of future big-data surveys, automated tools for unsupervised
discovery are becoming ever more necessary. In this work, we explore the
ability of deep generative networks for detecting outliers in astronomical
imaging datasets. The main advantage of such generative models is that they are
able to learn complex representations directly from the pixel space. Therefore,
these methods enable us to look for subtle morphological deviations which are
typically missed by more traditional moment-based approaches. We use a
generative model to learn a representation of expected data defined by the
training set and then look for deviations from the learned representation by
looking for the best reconstruction of a given object. In this first
proof-of-concept work, we apply our method to two different test cases. We
first show that from a set of simulated galaxies, we are able to detect
$\sim90\%$ of merging galaxies if we train our network only with a sample of
isolated ones. We then explore how the presented approach can be used to
compare observations and hydrodynamic simulations by identifying observed
galaxies not well represented in the models.
