Deeply-recursive ConvNet
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
- Seoul National University
- Organisation type
- Academia
- Country
- Korea (Republic of)
- Published
- 11 November 2016
- Authors
- Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee
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
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
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
- 2,744
Sources
Where this record came from and when it was last checked.
- Reference
- Deeply-Recursive Convolutional Network for Image Super-Resolution
- Last updated
- 25 May 2026
What the numbers mean
Background
Deeply-recursive ConvNet was published by Seoul National University, in the country recorded as Korea (Republic of), during November 2016. It comes out of an organisation categorised as academia.
It works in the domain of Vision, Image generation, and is recorded as performing the task of image super-resolution.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Deeply-recursive ConvNet — common questions
Deeply-recursive ConvNet— 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.
Deeply-recursive ConvNet— who created it?
It was published by Seoul National University, based in Korea (Republic of), an organisation categorised as academia.
Deeply-recursive ConvNet— 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.
Deeply-recursive ConvNet— what is it used for?
It works in the domain of Vision, Image generation, and is recorded as handling the task of 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.
Deeply-recursive ConvNet— 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.
Deeply-recursive ConvNet— is it open source?
The licensing 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.