Tensor-Based Continual Learning Method and Apparatus

A tensor-based continual learning method, wherein the method comprises:

• obtaining input data, wherein the input data comprises one or more of the following:
• a video, an image, a text, or speech;
• and
• inputting the input data into a first neural network to obtain a data processing result, wherein
• the first neural network is obtained through training of m tasks, and after training of an ith task ends, the neural network comprises A tensor cores, the A tensor cores comprise B tensor layers, and each of the B tensor layers comprises data of all of the A tensor cores in a same dimension;
• and after training of an (i+1)th task ends, a parameter in a part or all of the A tensor cores remains unchanged, or a parameter at a part of the B tensor layers remains unchanged, wherein m is a positive integer, and i is a positive integer less than or equal to m−1.

A tensor-based continual learning method, wherein the method comprises:

• receiving a plurality of model parameters sent by a plurality of user equipments respectively, wherein the plurality of user equipments comprise a first user equipment, and a model parameter sent by the first user equipment comprises a tensor core comprised in a first neural network;
• and
• updating a second neural network based on the plurality of model parameters, and processing to-be-processed data based on an updated second neural network to obtain a data processing result, wherein the to-be-processed data comprises one or more of the following:
• a picture, a video, speech, or a text;
• and
• the first neural network is obtained through training of m tasks, and after training of an ith task ends, the neural network comprises A tensor cores, the A tensor cores are divided into B tensor layers, and each of the B tensor layers comprises data of all of the A tensor cores in a same dimension;
• and after training of an (i+1)th task ends, C tensor cores and/or D tensor layers are added to the first neural network, and in the training of the (i+1)th task, parameters in the C tensor cores and/or parameters at the D tensor layers are updated, wherein m is a positive integer, and i is a positive integer less than or equal to m−1.

An electronic device, wherein the electronic device comprises at least one processor, a memory, and an interface circuit, the memory, the interface circuit, and the at least one processor are interconnected through a line, the memory stores instructions, the instructions instruct, when executed by the at least one processor, the at least one processor to perform the following operations:

• obtaining input data, wherein the input data comprises one or more of the following:
• a video, an image, a text, or speech;
• and
• inputting the input data into a first neural network to obtain a data processing result, wherein
• the first neural network is obtained through training of m tasks, and after training of an ith task ends, the neural network comprises A tensor cores, the A tensor cores are divided into B tensor layers, and each of the B tensor layers comprises data of all of the A tensor cores in a same dimension;
• and after training of an (i+1)th task ends, C tensor cores and/or D tensor layers are added to the first neural network, and in the training of the (i+1)th task, parameters in the C tensor cores and/or parameters at the D tensor layers are updated, wherein m is a positive integer, and i is a positive integer less than or equal to m−1.