pyCLAD: The universal framework for continual lifelong anomaly detection Article Swipe

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Kamil Faber , Bartłomiej Śnieżyński , Nathalie Japkowicz , Roberto Corizzo ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1016/j.softx.2024.101994

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.

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Computer Science

Anomaly Detection

Artificial Intelligence

Physics

Condensed Matter Physics

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Computer science Anomaly detection Anomaly (physics) Lifelong learning Artificial intelligence Physics Sociology Condensed matter physics Pedagogy

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.softx.2024.101994
OA Status: gold
References: 46
Related Works: 10
OpenAlex ID: https://openalex.org/W4405429950

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DOI: https://doi.org/10.1016/j.softx.2024.101994

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Title: pyCLAD: The universal framework for continual lifelong anomaly detection

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Language: en

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Publication year: 2024

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Publication date: 2024-12-16

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Authors: Kamil Faber, Bartłomiej Śnieżyński, Nathalie Japkowicz, Roberto Corizzo

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Landing page: https://doi.org/10.1016/j.softx.2024.101994

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OA URL: https://doi.org/10.1016/j.softx.2024.101994

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Concepts: Computer science, Anomaly detection, Anomaly (physics), Lifelong learning, Artificial intelligence, Physics, Sociology, Condensed matter physics, Pedagogy

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