Residual Dense 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
- Northeastern University,University of Rochester
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 24 February 2018
- Authors
- Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Image super-resolution
- Numerical format
- FP32
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
- 262,144,000 tokens
- Epochs
- 200
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
- 3,857
Sources
Where this record came from and when it was last checked.
- Reference
- Residual Dense Network for Image Super-Resolution
- Last updated
- 25 May 2026
What the numbers mean
Background
Residual Dense Network was published by Northeastern University,University of Rochester, in United States of America, in February 2018. academia,Academia is the category the publisher falls under.
It works in Vision, Image generation, and is recorded as doing image super-resolution.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
The training set ran to roughly 262,144,000 tokens.
Answers
Residual Dense Network — common questions
Who created Residual Dense Network?
Residual Dense Network was published by Northeastern University,University of Rochester, based in United States of America, categorised as academia,Academia.
When was Residual Dense Network released?
Residual Dense Network 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 Residual Dense Network used for?
Residual Dense Network works in Vision, Image generation, and is recorded as handling image super-resolution. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Residual Dense Network?
None. Residual Dense Network 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 Residual Dense Network open source?
The licensing for Residual Dense Network was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Residual Dense Network have?
No parameter count has been published for Residual Dense Network, which is why no memory or speed figure appears on this page.
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.