Using disentangled learning to train an interpretable deep learning model
US12223432B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 23, 2020 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Mar 26, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system of training an interpretable deep learning model includes receiving an input set of data, which may be complex. The input set of data is provided to deep learning model for feature extraction. In an exemplary embodiment, the deep learning model generates a disentangled latent space of features from the feature extraction. The features may comprise semantically meaningful data which is then provided to a low-complexity learning model. The low-complexity learning model generates output based on a specified task (for example, classification or regression). Being a low-complexity learning model provides confidence that the data output from the deep learning model is inherently interpretable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.