Neural network training
US12020475B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 21, 2022 |
| Grant date | Jun 25, 2024 |
| Priority date | — |
| Expiry date | Jan 1, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/58
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep neural network (DNN) can be trained based on a first training dataset that includes first images including annotated first objects. The DNN can be tested based on the first training dataset to determine first object predictions including first uncertainties. The DNN can be tested by inputting a second training dataset and outputting first object predictions including second uncertainties, wherein the second training dataset includes second images including unannotated second objects. A subset of images included in the second training dataset can be selected based on the second uncertainties, The second objects in the selected subset of images included in the second training dataset can be annotated. The DNN can be trained based on the selected subset of images included in the second training dataset including the annotated second objects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.