Data compression for machine learning tasks
US11256984B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 15, 2017 |
| Grant date | Feb 22, 2022 |
| Priority date | — |
| Expiry date | Nov 23, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04N19/91
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.