Contextual Anomaly Detection
4 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
These leaderboards are used to track progress in Contextual Anomaly Detection
Most implemented papers
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data
With 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 information.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text
lukasruff/CVDD-PyTorch
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ACL 2019
There 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.
Neural Contextual Anomaly Detection for Time Series
We 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 multivariate time series.
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally.
