Architecture search without using labels for deep autoencoders employed for anomaly detection
US11640536B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Apr 25, 2019 |
| Grant date | May 2, 2023 |
| Priority date | — |
| Expiry date | Jun 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and computer-readable storage media for defining an autoencoder architecture including a neural network, during training of the autoencoder, recording a loss value at each iteration to provide a plurality of loss values, the autoencoder being trained using a data set that is associated with a domain, and a learning rate to provide a trained autoencoder, calculating a penalty score using at least a portion of the plurality of loss values, the penalty score being based on a loss span penalty PLS, a convergence penalty PC, and a fluctuation penalty PF, comparing the penalty score P to a threshold penalty score to affect a comparison, and selectively employing the trained autoencoder for anomaly detection within the domain based on the comparison.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.