WGAN-GP
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
- Courant Institute of Mathematical Sciences,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms)
- Organisation type
- Academia,Academia
- Country
- United States of America, Canada
- Published
- 31 March 2017
- Authors
- Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville
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
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
MIT license: https://github.com/igul222/improved_wgan_training
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 10,824
Sources
Where this record came from and when it was last checked.
- Reference
- Improved Training of Wasserstein GANs
- Last updated
- 25 May 2026
What the numbers mean
Background
WGAN-GP was published by Courant Institute of Mathematical Sciences,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), in United States of America, in March 2017. academia,Academia is the category the publisher falls under.
It works in Image generation, and is recorded as doing image generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
WGAN-GP — common questions
Is WGAN-GP open source?
No. WGAN-GP has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does WGAN-GP have?
No parameter count has been published for WGAN-GP, which is why no memory or speed figure appears on this page.
Who created WGAN-GP?
WGAN-GP was published by Courant Institute of Mathematical Sciences,Mila - Quebec AI (originally Montreal Institute for Learning Algorithms), based in United States of America, categorised as academia,Academia.
When was WGAN-GP released?
WGAN-GP was published in March 2017. 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 WGAN-GP used for?
WGAN-GP 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 WGAN-GP?
None. WGAN-GP 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.
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.