System and method for improving robustness of pretrained systems in deep neural networks utilizing randomization and sample rejection
US12205349B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Mar 18, 2022 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | May 4, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.