Unsupervised High-level Feature Learner

Training compute

6×10¹⁷ FLOP

Parameters

1B

Published

Jul 12, 2012

Unsupervised High-level Feature Learner is an AI model developed by Google (United States), first published in July 2012. It works in the vision domain, on tasks such as image classification. It counts among the frontier models: the systems trained with the most compute of their moment.

Training it took an estimated 6×10¹⁷ FLOP of compute (estimation method: operation counting). The model has 1,000,000,000 parameters. It was trained on roughly 1.2T datapoints. The compute alone is estimated at $16 in 2023 dollars.

The reference paper has 2,909 citations. Epoch AI rates the confidence of this record as likely.

Full record

Organization: Google
Country of organization: United States
Domain: Vision
Task: Image classification
Training compute: 6×10¹⁷ FLOP
Compute estimation method: Operation counting
Parameters: 1,000,000,000
Dataset size: 1.2T
Training time: 72 h
Training cost (2023 USD): $16
Citations: 2,909
Epoch confidence: Likely