Domain adaptation for machine learning models
US11443193B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 4, 2020 |
| Grant date | Sep 13, 2022 |
| Priority date | — |
| Expiry date | Mar 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.