Simplifying convolutional neural networks using aggregated representations of images
US12380676B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 2, 2022 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Nov 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/70
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
One embodiment of the present invention sets forth a technique for simplifying a trained machine learning model. The technique includes determining a first set of images associated with a first output class predicted by the trained machine learning model. The technique also includes generating a first aggregated representation of the first set of images, wherein the first aggregated representation includes a first plurality of representative pixel values for a plurality of pixel locations included in the first set of images. The technique further includes generating a simplified representation of the trained machine learning model that includes a first mapping of the first aggregated representation to the first output class, wherein the first mapping indicates that the trained machine learning model predicts the first output class for one or more input images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.