Szczegóły publikacji
Opis bibliograficzny
VLAD: task-agnostic VAE-based lifelong anomaly detection / Kamil FABER, Roberto Corizzo, Bartłomiej ŚNIEŻYŃSKI, Nathalie Japkowicz // Neural Networks ; ISSN 0893-6080. — 2023 — vol. 165, s. 248-273. — Bibliogr. s. 271-273, Abstr. — Publikacja dostępna online od: 2023-05-27
Autorzy (4)
- AGHFaber Kamil
- Corizzo Roberto
- AGHŚnieżyński Bartłomiej
- Japkowicz Nathalie
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148090 |
|---|---|
| Data dodania do BaDAP | 2023-09-11 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.neunet.2023.05.032 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Neural Networks |
Abstract
Lifelong learning represents an emerging machine learning paradigm that aims at designing new methods providing accurate analyses in complex and dynamic real-world environments. Although a significant amount of research has been conducted in image classification and reinforcement learning, very limited work has been done to solve lifelong anomaly detection problems. In this context, a successful method has to detect anomalies while adapting to changing environments and preserving knowledge to avoid catastrophic forgetting. While state-of-the-art online anomaly detection methods are able to detect anomalies and adapt to a changing environment, they are not designed to preserve past knowledge. On the other hand, while lifelong learning methods are focused on adapting to changing environments and preserving knowledge, they are not tailored for detecting anomalies, and often require task labels or task boundaries which are not available in task-agnostic lifelong anomaly detection scenarios. This paper proposes VLAD, a novel VAE-based Lifelong Anomaly Detection method addressing all these challenges simultaneously in complex task-agnostic scenarios. VLAD leverages the combination of lifelong change point detection and an effective model update strategy supported by experience replay with a hierarchical memory maintained by means of consolidation and summarization. An extensive quantitative evaluation showcases the merit of the proposed method in a variety of applied settings. VLAD outperforms state-of-the-art methods for anomaly detection, presenting increased robustness and performance in complex lifelong settings.
