Browse State-of-the-Art › Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly
Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly
4 papers with code · 5 benchmarks · 5 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| Cats and Dogs (6 rows) | Shell-Renormalized | Shell Theory: A Statistical Model of Reality | code | — | Compare |
| STL-10 (6 rows) | Shell-Renormalized | Shell Theory: A Statistical Model of Reality | code | — | Compare |
| cifar10 (6 rows) | Shell-Renormalized | Shell Theory: A Statistical Model of Reality | code | — | Compare |
| MNIST (5 rows) | LVAD | Locally varying distance transform for unsupervised visual anomaly... | code | — | Compare |
| Fashion-MNIST (5 rows) | IF | Isolation forest | — | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
5 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
4 shown of 4 papers with code (6 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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30 Mar 2019 2 repositories listedThe encoder maps the data into a latent space, from which the RSR layer extracts the subspace.
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1 Jan 2018 2 repositories listedIn this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.
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23 Oct 2022 1 repository listedUnsupervised anomaly detection on image data is notoriously unstable.
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28 May 2021 1 repository listedThe foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate…
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