  We introduce an algorithmic method for population anomaly detection based on
gaussianization through an adversarial autoencoder. This method is applicable
to detection of `soft' anomalies in arbitrarily distributed highly-dimensional
data.
  A soft, or population, anomaly is characterized by a shift in the
distribution of the data set, where certain elements appear with higher
probability than anticipated. Such anomalies must be detected by considering a
sufficiently large sample set rather than a single sample.
  Applications include, but not limited to, payment fraud trends, data
exfiltration, disease clusters and epidemics, and social unrests. We evaluate
the method on several domains and obtain both quantitative results and
qualitative insights.
