Model training using incomplete indications of types of defects present in training images
US11250552B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 22, 2020 |
| Grant date | Feb 15, 2022 |
| Priority date | — |
| Expiry date | Aug 4, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30168
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Machine learning techniques are disclosed for training a model to identify each of multiple different classes in images, based on training data where each training image may not be labeled in a complete manner with respect to the classes. The disclosed training techniques use a new label value to indicate when a ground truth value is unknown for a particular class, and do not penalize the machine learning model for output predictions that do not match the label value representing unknown ground truth. The disclosed processes may, for example, be used to train a model to detect each multiple types of image defects based on incomplete information provided by human reviewers who accept and reject images based on whether any of the types of image defects are found.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.