Method, device, and storage medium for deep learning based domain adaptation with data fusion for aerial image data analysis
US12346809B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 21, 2021 |
| Grant date | Jul 1, 2025 |
| Priority date | — |
| Expiry date | Mar 29, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present disclosure provide a method, a device, and a storage medium for domain adaptation for efficient learning fusion (DAELF). The method includes acquiring data from a plurality of data sources of a plurality of sensors; for each of the plurality of sensors, training an auxiliary classifier generative adversarial network (AC-GAN) by a hardware processor with data from each data source of the plurality of data sources, thereby obtaining a trained feature extraction network and a trained label prediction network for each data source; forming a decision-level fusion network or a feature-level fusion network; and training the decision-level fusion network or the feature-level fusion network with a source-only mode or a generate to adapt (GTA) mode; and applying the trained decision-level fusion network or the trained feature-level fusion network to detect a target of interest.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.