  Automated detection of abnormalities in data has been studied in research
area in recent years because of its diverse applications in practice including
video surveillance, industrial damage detection and network intrusion
detection. However, building an effective anomaly detection system is a
non-trivial task since it requires to tackle challenging issues of the shortage
of annotated data, inability of defining anomaly objects explicitly and the
expensive cost of feature engineering procedure. Unlike existing appoaches
which only partially solve these problems, we develop a unique framework to
cope the problems above simultaneously. Instead of hanlding with ambiguous
definition of anomaly objects, we propose to work with regular patterns whose
unlabeled data is abundant and usually easy to collect in practice. This allows
our system to be trained completely in an unsupervised procedure and liberate
us from the need for costly data annotation. By learning generative model that
capture the normality distribution in data, we can isolate abnormal data points
that result in low normality scores (high abnormality scores). Moreover, by
leverage on the power of generative networks, i.e. energy-based models, we are
also able to learn the feature representation automatically rather than
replying on hand-crafted features that have been dominating anomaly detection
research over many decades. We demonstrate our proposal on the specific
application of video anomaly detection and the experimental results indicate
that our method performs better than baselines and are comparable with
state-of-the-art methods in many benchmark video anomaly detection datasets.
