Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

arXiv:2303.07557v2 Announce Type: replace
Abstract: Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong 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. However, limited efforts are dedicated to building foundations for lifelong anomaly detection, which provides intrinsically different challenges compared to the more widely explored classification setting. In this paper, we face this issue by exploring, motivating, …

abstract adapt anomaly anomaly detection arxiv behavior challenges continual cs.ai cs.lg detection domains dynamic environments however importance insights knowledge lifelong learning machine machine learning machine learning models perspectives trend type world