US 2025/0165805 A1 · META LEARNING METHOD OF DEEP LEARNING MODEL AND META LEARNING SYSTEM OF DEEP LEARNING MODEL

Abstract

This specification provides a meta learning method of a deep learning model and a meta learning system of a deep learning model, and relates to the field of deep learning technologies. The meta learning method of a deep learning model is applied to a cluster including N processing nodes, and the method includes: obtaining a training dataset, where the training dataset includes training samples corresponding to a plurality of tasks; and performing a plurality of times of iterative training on the deep learning model based on the training dataset in parallel by using the N processing nodes in the cluster, to obtain a meta learning parameter of the deep learning model, In each time of iterative training, each of the N processing nodes learns some parameters of the deep learning model by using some training samples in the training dataset, and the some training samples correspond to a same task.

Claims21

Dependent claims

A meta learning method of a deep learning model, wherein the method is applied to a cluster comprising N processing nodes, N is an integer greater than 1, and the method comprises:

obtaining a training dataset, wherein the training dataset comprises training samples corresponding to a plurality of tasks;
and
performing a plurality of times of iterative training on the deep learning model based on the training dataset in parallel by using the N processing nodes, to obtain a parameter of the deep learning model, wherein in each time of iterative training, each of the N processing nodes learns some parameters of the deep learning model by using some training samples in the training dataset, and the some training samples correspond to a same task in the plurality of tasks.

A computing device comprising a memory and a processor, wherein the memory stores executable instructions that, in response to execution by the processor, cause the computing device to implement a meta learning method of a deep learning model, wherein the method is applied to a cluster comprising N processing nodes, N is an integer greater than 1, and the method comprises:

obtaining a training dataset, wherein the training dataset comprises training samples corresponding to a plurality of tasks;
and
performing a plurality of times of iterative training on the deep learning model based on the training dataset in parallel by using the N processing nodes, to obtain a parameter of the deep learning model, wherein in each time of iterative training, each of the N processing nodes learns some parameters of the deep learning model by using some training samples in the training dataset, and the some training samples correspond to a same task in the plurality of tasks.

Dependencies are inferred from the claim text.

Meta learning method of deep learning model and meta learning system of deep learning model

Life of this patent

Meta learning method of deep learning model and meta learning system of deep learning model

Life of this patent

Abstract

Claims21