Computer vision systems and methods for end-to-end training of convolutional neural networks using differentiable dual-decomposition techniques
US12106481B2 · kind B2 · utility
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Key dates
| Filing date | Dec 14, 2020 |
| Grant date | Oct 1, 2024 |
| Priority date | — |
| Expiry date | Mar 23, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.
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