Spectrally Normalized GAN
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Preferred Networks Inc,Ritsumeikan University,National Institute of Informatics
- Organisation type
- Industry,Academia
- Country
- Japan
- Published
- 16 February 2018
- Authors
- Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation
- Task
- Image generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 2,560,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 4,967
Sources
Where this record came from and when it was last checked.
- Reference
- Spectral Normalization for Generative Adversarial Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
Spectrally Normalized GAN was published by Preferred Networks Inc,Ritsumeikan University,National Institute of Informatics, in Japan, in February 2018. industry,Academia is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It was trained on about 2,560,000 tokens of text.
Answers
Spectrally Normalized GAN — common questions
How many parameters does Spectrally Normalized GAN have?
No parameter count has been published for Spectrally Normalized GAN, which is why no memory or speed figure appears on this page.
Who created Spectrally Normalized GAN?
Spectrally Normalized GAN was published by Preferred Networks Inc,Ritsumeikan University,National Institute of Informatics, based in Japan, categorised as industry,Academia.
When was Spectrally Normalized GAN released?
Spectrally Normalized GAN was published in February 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Spectrally Normalized GAN used for?
Spectrally Normalized GAN works in Image generation, and is recorded as handling image generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Spectrally Normalized GAN?
None. Spectrally Normalized GAN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Spectrally Normalized GAN open source?
The licensing for Spectrally Normalized GAN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.