Szczegóły publikacji
Opis bibliograficzny
Continual anomaly detection with pyCLAD / Kamil FABER, Roberto Corizzo // W: Machine Learning and Knowledge Discovery in Databases : Applied Data Science Track, Demo Track and Industrial Track : European Conference, ECML PKDD 2026 : Naples, Italy, September 7–11, 2026 : proceedings , Pt. 10 / eds. Mitra Baratchi, [et al.]. — Cham : Springer Nature, cop. 2027. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; vol. 16950. Lecture Notes in Artificial Intelligence ). — ISBN: 978-3-032-37684-8; e-ISBN: 978-3-032-37685-5. — S. 297–301. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-09-08
Autorzy (2)
- AGHFaber Kamil
- Corizzo Roberto
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 170053 |
|---|---|
| Data dodania do BaDAP | 2026-10-09 |
| DOI | 10.1007/978-3-032-37685-5_21 |
| Rok publikacji | 2027 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database 2026 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Continual anomaly detection is gaining attention in many domains where the notion of normality evolves over time and models must adapt without forgetting previously learned knowledge. To this end, we demonstrate pyCLAD, an open-source Python framework for continual anomaly detection. pyCLAD provides reusable abstractions and implementations for scenarios, strategies, models, metrics, and experiment analysis. The proposed library is the first framework to address the intersection of anomaly detection and continual learning, as existing libraries largely target offline anomaly detection or supervised continual learning. pyCLAD is available as a pypi package https://pypi.org/project/pyclad/.
