Anomaly detection with predictive normalization
US10964011B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 4, 2019 |
| Grant date | Mar 30, 2021 |
| Priority date | — |
| Expiry date | Dec 4, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
A method is provided for model training to detect defective products. The method includes sampling training images of a product to (i) extract image portions therefrom made of a center patch and its context and (ii) black-out the center patch. The method further includes performing unsupervised back-propagation training of a Contextual Auto-Encoder (CAE) model using (i) the image portions with the blacked-out center patch as an input and, (ii) the center patch as a target output and, (iii) an image-based loss function, to obtain a trained CAE model. The method also includes sampling positive and negative center-patch-sized portions from the training images. The method additionally includes normalizing, using the trained CAE model, the positive and negative center-patch-sized portions. The method further includes performing supervised training of a classifier model using the normalized positive and negative center-patch-sized portions to obtain a trained supervised classifier model for detecting the defective products.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.