https://github.com/shubhomoydas/ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
https://github.com/shubhomoydas/ad_examples

active-learning adversarial-attacks anogan anomaly-detection autoencoder concept-drift ensemble-learning explaination gan generative-adversarial-network graph-convolutional-networks interpretability lstm nettack rnn streaming time-series timeseries trees unsuperivsed

Last synced: about 2 months ago
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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

• Host: GitHub
• URL: https://github.com/shubhomoydas/ad_examples
• Owner: shubhomoydas
• License: mit
• Created: 2017-11-03T16:30:18.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-05-22T02:17:55.000Z (7 months ago)
• Last Synced: 2024-08-01T02:25:51.602Z (4 months ago)
• Topics: active-learning, adversarial-attacks, anogan, anomaly-detection, autoencoder, concept-drift, ensemble-learning, explaination, gan, generative-adversarial-network, graph-convolutional-networks, interpretability, lstm, nettack, rnn, streaming, time-series, timeseries, trees, unsuperivsed
• Language: Python
• Homepage:
• Size: 125 MB
• Stars: 838
• Watchers: 37
• Forks: 186
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-AIOps - Anomaly Detection Examples