DTN (Domain Transfer Network)
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
- Facebook AI Research
- Organisation type
- Industry
- Country
- United States of America, France
- Published
- 7 November 2016
- Authors
- Yaniv Taigman, Adam Polyak, Lior Wolf
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Image generation
- Approach
- Supervised
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
- 2,000,000 tokens
"For face images, we use a set s of one million random images without identity information."
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
- Unreleased
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
Sources
Where this record came from and when it was last checked.
- Reference
- Unsupervised Cross-Domain Image Generation
- Last updated
- 11 February 2026
What the numbers mean
Background
DTN (Domain Transfer Network) was published by Facebook AI Research, in the country recorded as United States of America, during November 2016. It comes out of an organisation categorised as industry.
It works in the domain of Vision, Image generation, and is recorded as performing the task of image generation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 2,000,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
DTN (Domain Transfer Network) — common questions
DTN (Domain Transfer Network)— what is it used for?
It works in the domain of Vision, Image generation, and is recorded as handling the task of image generation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
DTN (Domain Transfer Network)— what GPU do I need to run it?
None. This 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.
DTN (Domain Transfer Network)— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
DTN (Domain Transfer Network)— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
DTN (Domain Transfer Network)— who created it?
It was published by Facebook AI Research, based in United States of America, an organisation categorised as industry.
DTN (Domain Transfer Network)— when was it released?
It was published in November 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.