ROBUST AND ADAPTIVE ANOMALY DETECTION WITH DEEP LEARNING

Abstract

Anomaly detection is the task of detecting samples within a set of data that deviate from the normal or expected behaviour. As data grows in abundance and importance for decision-making in many industries, anomaly detection methods that are fast, accurate and automated are increasingly vital. There has been growing interest in the use of deep learning models for anomaly detection. Deep learning has been extremely successful in improving state-of-the-art performance in a wide variety of data-driven tasks in recent years. However, anomaly detection remains a particularly difficult task and many of its unique challenges are yet to be adequately solved with existing methods. In this thesis, we focus on two such challenges: their robustness and their adaptivity. A robust model is one which is not influenced by a certain characteristic of the dataset and therefore variation in this characteristic does not significantly change the model outputs. Conversely, an adaptive model is more sensitive to this characteristic and the output of the model is changed significantly by the same variation.