  Unlike conventional anomaly detection research that focuses on point
anomalies, our goal is to detect anomalous collections of individual data
points. In particular, we perform group anomaly detection (GAD) with an
emphasis on irregular group distributions (e.g. irregular mixtures of image
pixels). GAD is an important task in detecting unusual and anomalous phenomena
in real-world applications such as high energy particle physics, social media,
and medical imaging. In this paper, we take a generative approach by proposing
deep generative models: Adversarial autoencoder (AAE) and variational
autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group
anomalies using point-wise input data where group memberships are known a
priori. We conduct extensive experiments to evaluate our models on real-world
datasets. The empirical results demonstrate that our approach is effective and
robust in detecting group anomalies.
