Transfer Learning
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 Zagreb
- Organisation type
- Academia
- Country
- Croatia
- Published
- 1 July 1976
- Authors
- Stevo Bozinovski, Ante Fulgos
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Digit recognition, Character recognition (OCR)
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
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- Historical significance
- Record confidence
- Unknown
The first paper on transfer learning
Sources
Where this record came from and when it was last checked.
- Reference
- The influence of pattern similarity and transfer learning on the base perceptron training.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Transfer Learning was published by University of Zagreb, in the country recorded as Croatia, during July 1976. It comes out of an organisation categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of digit recognition, Character recognition (OCR).
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Its inclusion criterion: historical significance.
Answers
Transfer Learning — common questions
Transfer Learning— 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.
Transfer Learning— who created it?
It was published by University of Zagreb, based in Croatia, an organisation categorised as academia.
Transfer Learning— when was it released?
It was published in July 1976. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Transfer Learning— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of digit recognition, Character recognition (OCR). Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Transfer Learning— 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.
Transfer Learning— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
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.