Methods and systems for budgeted and simplified training of deep neural networks
US11263490B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 7, 2017 |
| Grant date | Mar 1, 2022 |
| Priority date | — |
| Expiry date | Apr 19, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.