Elastic weight consolidation
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
- DeepMind
- Organisation type
- Industry
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 2 December 2016
- Authors
- James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Games
- Task
- Image classification, Atari
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.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 9,809
Sources
Where this record came from and when it was last checked.
- Reference
- Overcoming catastrophic forgetting in neural networks
- Last updated
- 25 May 2026
What the numbers mean
About this model
Elastic weight consolidation was published by DeepMind, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during December 2016. The category the publisher falls under is industry.
It works in the domain of Vision, Games, and is recorded as performing the task of image classification, Atari.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Elastic weight consolidation — common questions
Elastic weight consolidation— who created it?
It was published by DeepMind, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as industry.
Elastic weight consolidation— when was it released?
It was published in December 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.
Elastic weight consolidation— what is it used for?
It works in the domain of Vision, Games, and is recorded as handling the task of image classification, Atari. These are the areas it was designed around; they describe intent rather than a hard boundary.
Elastic weight consolidation— 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.
Elastic weight consolidation— 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.
Elastic weight consolidation— 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.
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.