pyCLAD: The universal framework for continual lifelong anomaly detection
Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new challenges in dynamic environments while retaining past know...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024003649 |
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| author | Kamil Faber Bartlomiej Sniezynski Nathalie Japkowicz Roberto Corizzo |
| author_facet | Kamil Faber Bartlomiej Sniezynski Nathalie Japkowicz Roberto Corizzo |
| author_sort | Kamil Faber |
| collection | DOAJ |
| description | Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new challenges in dynamic environments while retaining past knowledge. In this paper, we propose pyCLAD, the first software framework providing foundations for the design of new continual anomaly detection scenarios, strategies, and evaluation protocols, while streamlining the execution of experimental workflows with high reproducibility standards. |
| format | Article |
| id | doaj-art-2fecdc5cb36b49eb8048a85d75dea4ad |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-2fecdc5cb36b49eb8048a85d75dea4ad2025-08-20T02:13:49ZengElsevierSoftwareX2352-71102025-02-012910199410.1016/j.softx.2024.101994pyCLAD: The universal framework for continual lifelong anomaly detectionKamil Faber0Bartlomiej Sniezynski1Nathalie Japkowicz2Roberto Corizzo3AGH University of Krakow, Department of Computer Science, Adama Mickiewicza 30, Krakow, 30-059, PolandAGH University of Krakow, Department of Computer Science, Adama Mickiewicza 30, Krakow, 30-059, PolandAmerican University, Department of Computer Science, 4400 Massachusetts Ave NW, Washington, 20016, DC, United StatesAGH University of Krakow, Department of Computer Science, Adama Mickiewicza 30, Krakow, 30-059, Poland; American University, Department of Computer Science, 4400 Massachusetts Ave NW, Washington, 20016, DC, United States; Corresponding author at: American University, Department of Computer Science, 4400 Massachusetts Ave NW, Washington, 20016, DC, United States.Anomaly detection is a recognized problem with high significance and impact in many real-world settings. Continual anomaly detection is an emerging paradigm that allows for the design of anomaly detection methods capable of adapting to new challenges in dynamic environments while retaining past knowledge. In this paper, we propose pyCLAD, the first software framework providing foundations for the design of new continual anomaly detection scenarios, strategies, and evaluation protocols, while streamlining the execution of experimental workflows with high reproducibility standards.http://www.sciencedirect.com/science/article/pii/S2352711024003649Continual anomaly detectionLifelong anomaly detectionContinual learningAnomaly detectionSoftware |
| spellingShingle | Kamil Faber Bartlomiej Sniezynski Nathalie Japkowicz Roberto Corizzo pyCLAD: The universal framework for continual lifelong anomaly detection SoftwareX Continual anomaly detection Lifelong anomaly detection Continual learning Anomaly detection Software |
| title | pyCLAD: The universal framework for continual lifelong anomaly detection |
| title_full | pyCLAD: The universal framework for continual lifelong anomaly detection |
| title_fullStr | pyCLAD: The universal framework for continual lifelong anomaly detection |
| title_full_unstemmed | pyCLAD: The universal framework for continual lifelong anomaly detection |
| title_short | pyCLAD: The universal framework for continual lifelong anomaly detection |
| title_sort | pyclad the universal framework for continual lifelong anomaly detection |
| topic | Continual anomaly detection Lifelong anomaly detection Continual learning Anomaly detection Software |
| url | http://www.sciencedirect.com/science/article/pii/S2352711024003649 |
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