Deep 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 Toronto
- Organisation type
- Academia
- Country
- Canada
- Published
- 29 April 2011
- Authors
- A. Krizhevsky, Geoffrey E. Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image representation
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.
- Parameters
- 139.8M
- Training data
- 4,915,200,000 tokens
- Epochs
- 85
2*(3072*8192+8192*4096+4096*2048+2048*1024+1024*512+512*256+256*128+128*64+64*28)=139808256 "n each autoencoder, the hidden layers halve in size until they reach the desired size, except that we use 28 instead of 32"
"We train on 1.6 million 32 × 32 color images"
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.7 × 10¹⁶ FLOP
- How it was established
- Hardware
48*60*60*708500000000*0.3=36728640000000000=3.7e16 GTX 285 with 708.5 GFLOP/s
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 285
- Chips used
- 1
- Wall-clock time
- 48 hours
- Power draw
- 246 W
"The entire training procedure for each autoencoder took about 2 days on an Nvidia GTX 285 GPU."
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Frontier model
- Yes
- Why it is tracked
- Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Using very deep autoencoders for content-based image retrieval
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Deep Autoencoders was published by University of Toronto, in the country recorded as Canada, during April 2011. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of image representation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training run consumed about 3.7 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX 285. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 4,915,200,000 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
Deep Autoencoders — common questions
Deep Autoencoders— how many parameters does it have?
It has a parameter count of 139.8M. 2*(3072*8192+8192*4096+4096*2048+2048*1024+1024*512+512*256+256*128+128*64+64*28)=139808256 "n each autoencoder, the hidden layers halve in size until they reach the desired size, except that we use 28 instead of 32". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Deep Autoencoders— who created it?
It was published by University of Toronto, based in Canada, an organisation categorised as academia.
Deep Autoencoders— when was it released?
It was published in April 2011. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Deep Autoencoders— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image representation. 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.
Deep Autoencoders— how much compute was used to train it?
Training consumed around 3.7 × 10¹⁶ FLOP, on hardware recorded as NVIDIA GeForce GTX 285. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Deep 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.
Deep 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.
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.