A3
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications. (by Fraunhofer-AISEC)
awesome-TS-anomaly-detection
List of tools & datasets for anomaly detection on time-series data. (by rob-med)
A3 | awesome-TS-anomaly-detection | |
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1 | 72 | |
9 | 2,811 | |
- | - | |
0.0 | 0.0 | |
almost 2 years ago | 2 months ago | |
Python | ||
GNU General Public License v3.0 or later | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
A3
Posts with mentions or reviews of A3.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2020-12-31.
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[P] Looking for Resources on Anomaly Detection
As the code is available as well, you can directly test the approach and see if it is a viable solution for the telemetry data: https://github.com/Fraunhofer-AISEC/A3/
awesome-TS-anomaly-detection
Posts with mentions or reviews of awesome-TS-anomaly-detection.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2020-12-31.
What are some alternatives?
When comparing A3 and awesome-TS-anomaly-detection you can also consider the following projects:
alibi-detect - Algorithms for outlier, adversarial and drift detection
Awesome-Geospatial - Long list of geospatial tools and resources
NAB - The Numenta Anomaly Benchmark
openHistorian - The Open Source Time-Series Data Historian
awesome-metric-learning - 😎 A curated list of awesome practical Metric Learning and its applications
Netdata - The open-source observability platform everyone needs
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
MLflow - Open source platform for the machine learning lifecycle
A3 vs alibi-detect
awesome-TS-anomaly-detection vs Awesome-Geospatial
A3 vs NAB
awesome-TS-anomaly-detection vs openHistorian
awesome-TS-anomaly-detection vs awesome-metric-learning
awesome-TS-anomaly-detection vs Netdata
awesome-TS-anomaly-detection vs NAB
awesome-TS-anomaly-detection vs pyod
awesome-TS-anomaly-detection vs MLflow