Multi-task segmented learning models
US12190245B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 11, 2019 |
| Grant date | Jan 7, 2025 |
| Priority date | — |
| Expiry date | Oct 3, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/394
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are provided for implementing training of learning models, including obtaining a pre-trained weight set for a learning model on a sample dataset and on a first loss function; selecting at least two tasks having heterogeneous features to be computed by a reference model; obtaining a reference dataset for the at least two tasks; designating a second loss function for feature embedding between the heterogeneous features of the at least two tasks; training the learning model on the first loss function and training the reference model on the second loss function, in turn; and updating the weight set based on a feature embedding learned by the learning model and a feature embedding learned by the reference model, in turn. Methods and systems of the present disclosure may alleviate computational overhead incurred by executing the learning model and loading different weight sets at a central network of the model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.