When and where? Monday, June 30, 2025, 11:30-13:00 - Marinetti Room.
Abstract. Anomaly detection is paramount in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions, and network traffic.
Current research in anomaly detection leverages offline learning working with static data or online learning, focusing on constant adaptation to evolving data. At the same time, continual learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge.
Although this aspect could be beneficial to build effective and robust anomaly detection models, continual learning research is mainly dedicated to proposing new model update strategies in image classification and reinforcement learning domains.
Moreover, anomaly detection provides unique challenges, such as an evolving normal class and limited availability of anomalies, which significantly differ from the landscape and scenarios of continual image classification and reinforcement learning.
Outline. In this tutorial, we want to introduce and motivate the problem of continual anomaly detection as well as provide foundations about scenarios, strategies, and metrics. Moreover, we want to showcase how to approach the problem of continual anomaly detection with code examples and hands-on practice.
Prerequisite knowledge. The tutorial requires basic knowledge of machine learning and anomaly detection. All other required notions will be provided during the tutorial.
✨Check out our Workshop on Open-World Anomaly Detection at ICDM 2025! ✨
Materials
Tutorial slides: Link.
Getting Started in PyCLAD: Link
