Anomaly Detection by Context Contrasting

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.