Methods and apparatus for discriminative semantic transfer and physics-inspired optimization of features in deep learning
US12079713B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 3, 2023 |
| Grant date | Sep 3, 2024 |
| Priority date | — |
| Expiry date | May 3, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and apparatus for discrimitive semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.