Cutout-regularized 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
- University of Guelph,Vector Institute,CIFAR AI Research
- Organisation type
- Academia,Academia,Research collective
- Country
- Canada
- Published
- 15 August 2017
- Authors
- Terrance DeVries, Graham W. Taylor
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- 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
- 604,388 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
- 4,263
Sources
Where this record came from and when it was last checked.
- Reference
- Improved Regularization of Convolutional Neural Networks with Cutout
- Last updated
- 25 May 2026
What the numbers mean
About this model
Cutout-regularized net was published by University of Guelph,Vector Institute,CIFAR AI Research, in the country recorded as Canada, during August 2017. It comes out of an organisation categorised as academia,Academia,Research collective.
It works in the domain of Vision, 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.
Training and provenance
It was trained on a corpus of about 604,388 tokens of text.
Answers
Cutout-regularized net — common questions
Cutout-regularized 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.
Cutout-regularized 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.
Cutout-regularized net— who created it?
It was published by University of Guelph,Vector Institute,CIFAR AI Research, based in Canada, an organisation categorised as academia,Academia,Research collective.
Cutout-regularized net— when was it released?
It was published in August 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Cutout-regularized net— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. 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.
Cutout-regularized 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.
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.