Denoising Autoencoders
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
- University of Montreal / Université de Montréal
- Organisation type
- Academia
- Country
- Canada
- Published
- 5 July 2008
- Authors
- Pascal Vincent, Hugo Larechelle, Yoshua Bengio, Pierre- Antoine Manzagol
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Other
- Task
- Image classification
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
- 7,840,000 tokens
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
- 7,894
Sources
Where this record came from and when it was last checked.
- Reference
- Extracting and Composing Robust Features with Denoising Autoencoders
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
Denoising Autoencoders was published by University of Montreal / Université de Montréal, in the country recorded as Canada, during July 2008. The publishing organisation is categorised as academia.
It works in the domain of Other, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
Training consumed a corpus of around 7,840,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Denoising Autoencoders — common questions
Denoising Autoencoders— 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.
Denoising Autoencoders— 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.
Denoising Autoencoders— 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.
Denoising Autoencoders— who created it?
It was published by University of Montreal / Université de Montréal, based in Canada, an organisation categorised as academia.
Denoising Autoencoders— when was it released?
It was published in July 2008. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Denoising Autoencoders— what is it used for?
It works in the domain of Other, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.