Generating image features based on robust feature-learning
US9830526B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 26, 2016 |
| Grant date | Nov 28, 2017 |
| Priority date | — |
| Expiry date | May 26, 2036 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/178
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.