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Contextual Anomaly Detection
5 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events. Contextual Anomaly Detection is formulated such that the data contains two types of attributes, behavioral and contextual attributes. Behavioral attributes are attributes that relate directly to the process of interest whereas contextual attributes relate to exogenous but highly affecting factors in relation to the process. Generally the behavioral attributes are conditional on the contextual attributes. Source: Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models
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5 shown of 5 papers with code (13 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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27 Feb 2025 1 repository listedUsing simulations of a swarm's normal behavior, a normalizing flow is trained to predict the likelihood of a robot motion within the current context of its environment.
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22 Feb 2023 1 repository listedTraditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally.
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16 Jul 2021 1 repository listedWe introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and…
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1 Jul 2019 1 repository listedThere exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
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17 Feb 2019 1 repository listedWith the increasing importance of online communities, discussion forums, and customer reviews, Internet "trolls" have proliferated thereby making it difficult for information seekers to find relevant and correct…
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