Systems and methods for deep multi-task learning for embedded machine vision applications
US11527074B1 · kind B1 · utility
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Key dates
| Filing date | Nov 24, 2021 |
| Grant date | Dec 13, 2022 |
| Priority date | — |
| Expiry date | Nov 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/96
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method includes receiving data generated using at least one sensor of a vehicle; and simultaneously performing multiple different prediction tasks on the data using a multi-task neural network, wherein the multi-task neural network comprises at least one shared parameter inference matrix comprising parameters shared between the multiple different prediction tasks, and the at least one shared parameter inference matrix was over-parameterized during training into at least one shared parameter matrix and multiple task-specific parameter matrices, each of the multiple task-specific parameter matrices being associated with a different one of the multiple different tasks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.