Symmetric Residual Encoder-Decoder Net
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
- Nanjing University,University of Adelaide
- Organisation type
- Academia,Academia
- Country
- China, Australia
- Published
- 30 March 2016
- Authors
- Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
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
- 1,250,000,000 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
- 1,184
Sources
Where this record came from and when it was last checked.
- Reference
- Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Symmetric Residual Encoder-Decoder Net was published by Nanjing University,University of Adelaide, in the country recorded as China, during March 2016. The category the publisher falls under is academia,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.
What went into building it
It was trained on a corpus of about 1,250,000,000 tokens of text.
Answers
Symmetric Residual Encoder-Decoder Net — common questions
Symmetric Residual Encoder-Decoder Net— when was it released?
It was published in March 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.
Symmetric Residual Encoder-Decoder Net— 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. These are the areas it was designed around; they describe intent rather than a hard boundary.
Symmetric Residual Encoder-Decoder Net— 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.
Symmetric Residual Encoder-Decoder Net— 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.
Symmetric Residual Encoder-Decoder Net— 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.
Symmetric Residual Encoder-Decoder Net— who created it?
It was published by Nanjing University,University of Adelaide, based in China, an organisation categorised as academia,Academia.
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.