visionfamily
GANomaly
Semi-supervised anomaly detection via adversarial training. Paper by Akcay et al. (2018).
Levelfamily
Classvision
Parametersnot in the notes
Contextnot in the notes
Released17 May 2018
Opennessopen source
Runs onnot in the notes
Statusactive
Identity
- Developer
- Durham University
- Family
- not in the notes
- Released
- 17 May 2018 sourcesannouncement page
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other Encoder-decoder-encoder GAN
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- image
- Output
- image
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open source
- Licence
- Apache License 2.0 sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- Licence is that of the Anomalib implementation (Apache-2.0).
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperDurham Universityuniversity, UK
Durham Universityuniversity, UKModelGANomalyDurham University, vision
GANomalyDurham University, visionUse CasesVisual Quality Inspection and Defect DetectionIndustrialSafety (PPE, Hazards, Fatigue)IndustrialUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026
PT Bukit Asam TbkIn production, 9 Sep 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025
PT Kilang Pertamina Internasional (Pertamina refineries)In production, 17 Oct 2025+5 moreDeployments of these use cases; none names this model.
PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026PT Bukit Asam TbkIn production, 9 Sep 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025PT Kilang Pertamina Internasional (Pertamina refineries)In production, 17 Oct 2025+5 moreDeployments of these use cases; none names this model.Organisations 1
Sources
- arxiv.org/abs/1805.06725 https://arxiv.org/abs/1805.06725
- github.com/open-edge-platform/anomalib https://github.com/open-edge-platform/anomalib
- anomalib.readthedocs.io/en/latest/markdown/guides/reference/models/im... https://anomalib.readthedocs.io/en/latest/markdown/guides/reference/models/image/index.html
- arxiv.org/html/1805.06725 https://arxiv.org/html/1805.06725
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "GANomaly", https://aurion.semai.id/models/ganomaly/, records of October 2026.