  Anomaly Detection focuses on identifying samples that deviate from the norm.
When working with high-dimensional data such as images, a crucial requirement
for detecting anomalous patterns is learning lower-dimensional representations
that capture normal concepts seen during training. Recent advances in
self-supervised learning have shown great promise in this regard. However, many
of the most successful self-supervised anomaly detection methods assume prior
knowledge about the structure of anomalies and leverage synthetic anomalies
during training. Yet, in many real-world applications, we do not know what to
expect from unseen data, and we can solely leverage knowledge about normal
data. In this work, we propose Con2, which addresses this problem by setting
normal training data into distinct contexts while preserving its normal
properties, letting us observe the data from different perspectives. Unseen
normal data consequently adheres to learned context representations while
anomalies fail to do so, letting us detect them without any knowledge about
anomalies during training. Our experiments demonstrate that our approach
achieves state-of-the-art performance on various benchmarks while exhibiting
superior performance in a more realistic healthcare setting, where knowledge
about potential anomalies is often scarce.
