Methods › General › Semi-Supervised Learning Methods › DifferNet
DifferNet
Introduced by Marco Rudolph et al. in Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The archive carries no description for this method.
Papers archive 2025-07-28
5 shown of 5, 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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An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey 26 Feb 2024 · 0 repositories · arXiv:2402.17045
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APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks 13 Feb 2024 · 0 repositories · arXiv:2402.08244
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Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study 5 Nov 2023 · 1 repository · arXiv:2311.02747
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Computer Vision and Normalizing Flow-Based Defect Detection 12 Dec 2020 · 1 repository · arXiv:2012.06737
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Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows 28 Aug 2020 · 3 repositories · arXiv:2008.12577
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
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