Domain adaptation by multi-noising stacked marginalized denoising encoders
US9916542B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 2, 2016 |
| Grant date | Mar 13, 2018 |
| Priority date | — |
| Expiry date | May 11, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/35
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning method operates on training instances from a plurality of domains including one or more source domains and a target domain. Each training instance is represented by values for a set of features. Domain adaptation is performed using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers. Each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probabilities that are non-uniform over at least one of the set of features and the domains. A classifier is learned on the training instances transformed using the stack of domain adaptation transform layers. Thereafter, a label prediction is generated for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.