Methods and systems for cross-domain few-shot classification
US12217187B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 17, 2021 |
| Grant date | Feb 4, 2025 |
| Priority date | — |
| Expiry date | Dec 7, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and media for training deep neural networks for cross-domain few-shot classification are described. The methods comprise an encoder and a decoder of a deep neural network. The training of the autoencoder comprises two training stages. For each iteration in the first training stage, a batch of data samples from the source dataset are sampled and fed to the encoder to generate a plurality of source feature maps, then determining a first training stage loss, which updates the autoencoder's parameters. For each iteration in the second training stage, the novel dataset is split into a support set and a query set. The support set is fed to the encoder to determine a prototype for each class label. The query set is also fed to the encoder to calculate a query set metric classification loss. The query set metric classification loss updates the autoencoder's parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.