System and method for layer-wise training of deep neural networks
US10163197B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 30, 2017 |
| Grant date | Dec 25, 2018 |
| Priority date | — |
| Expiry date | Aug 3, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
System and method for layer-wise training of deep neural networks (DNNs) are disclosed. In an embodiment, multiple labelled images are received at a layer of multiple layers of a DNN. Further, the labelled images are pre-processed. The pre-processed images are then transformed based on a predetermined weight matrix to obtain feature representation of the pre-processed images at the layer, the feature representation comprise feature vectors and associated labels. Furthermore, kernel similarity between the feature vectors is determined based on a predefined kernel function. Moreover, a Gaussian kernel matrix is determined based on the kernel similarity. In addition, an error function is computed based on the predetermined weight matrix and the Gaussian kernel matrix. Also, a weight matrix associated with the layer is computed based on the error function and predetermined weight matrix, thereby training the layer of the multiple layers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.