Prototypical network algorithms for few-shot learning
US10963754B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 27, 2018 |
| Grant date | Mar 30, 2021 |
| Priority date | — |
| Expiry date | Oct 17, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for training an embedding using a limited training set are described. In some examples, the embedding is trained by generating a plurality of vectors from a random sample of the limited set of training data classes using a layer of the particular machine learning classification model, randomly selecting samples from the plurality of vectors into a set of samples, computing at least one distance for each sampled class from a center parameter for the class using the set of samples, generating a discrete probability distribution over the classes for a query point based on distances to a center parameter for each of the classes in the embedding space, calculating a loss value for the modified prototypical network, the calculation of the loss value being for a fixed geometry of the embedding space and including a measure of the difference between distributions, and back propagating.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.