Methods › Computer Vision › Generative Adversarial Networks › Outlier Generation
Outlier Generation in Tabular Data
Outlier Generation
Introduced by Azizjon Azimi et al. in zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
A group of methods designed to generate outliers in tabular data, emphasizing the importance of such techniques as supported by extreme value theory. These methods are crucial for modeling, analyzing, and understanding anomalous or rare events within structured datasets.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation 28 Oct 2024 · 0 repositories · arXiv:2410.20808
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Binary Classification | 1 |
| Generative Adversarial Network | 1 |
| Synthetic Data Evaluation | 1 |
| Synthetic Data Generation | 1 |
| Synthetic Outliers Evaluation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections