Method for training neural network model and apparatus
AssigneeHuawei Technologies Co., Ltd.
InventorsTao Ma, Qing Su, Ying Jin
Life of this patent
Estimating the term…
Abstract
This disclosure provides methods and apparatuses for training a neural network model. One example method performed by a terminal device includes: obtaining annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model, training a second neural network model by using the annotation data of the service to obtain a trained second neural network model, and updating a first neural network model based on the trained second neural network model.
Claims18
A method for training a neural network model, comprising:
- obtaining, by a terminal device, annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model;
- training, by the terminal device, the second neural network model by using the annotation data to obtain a trained second neural network model;
- and
- updating, by the terminal device, the first neural network model based on the trained second neural network model.
A terminal device, comprising:
- a memory storing instructions;
- and
- one or more processors in communication with the memory for executing the instructions to perform operations comprising:
- obtaining annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model;
- training the second neural network model by using the annotation data to obtain a trained second neural network model;
- and
- updating the first neural network model based on the trained second neural network model.
A non-transitory computer-readable medium storing computer instructions executable by one or more processors of a terminal device to perform operations comprising:
- obtaining annotation data of a service, wherein the service is to be processed by a first neural network model and a second neural network model, and wherein precision of the first neural network model is lower than precision of the second neural network model;
- training the second neural network model by using the annotation data to obtain a trained second neural network model;
- and
- updating the first neural network model based on the trained second neural network model.
Dependencies are inferred from the claim text.