System for fast and accurate visual domain adaptation
US10839269B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 20, 2020 |
| Grant date | Nov 17, 2020 |
| Priority date | — |
| Expiry date | Mar 20, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. But through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. GAN (Generative Adversarial Networks) loss is widely used in adversarial adaptation learning methods to reduce a across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, an adaptation algorithm and system called as Generative Adversarial Distribution Matching (GADM) is implemented. In GADM, the objective function is improved by taking cross-domain discrepancy distance into consideration, and further minimize the difference through the competition between the generator and discriminator, thereby greatly decreasing the cross-domain distribution difference. Even when the performance of its generat…
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.