Object recognition neural network training using multiple data sources
US12430903B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 28, 2021 |
| Grant date | Sep 30, 2025 |
| Priority date | — |
| Expiry date | Aug 6, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object recognition neural network using multiple data sources. One of the methods includes receiving training data that includes a plurality of training images from a first source and images from a second source. A set of training images are obtained from the training data. For each training image in the set of training images, contrast equalization is applied to the training image to generate a modified image. The modified image is processed using the neural network to generate an object recognition output for the modified image. A loss is determined based on errors between, for each training image in the set, the object recognition output for the modified image generated from the training image and ground-truth annotation for the training image. Parameters of the neural network are updated based on the determined loss.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.