Classification model training method and related apparatus

A classification method, comprising:

• obtaining to-be-classified data;
• generating at least one piece of target data based on the to-be-classified data, wherein the at least one piece of target data is obtained based on the to-be-classified data, and a data amount of the at least one piece of target data is less than a data amount of the to-be-classified data;
• inputting the to-be-classified data and the at least one piece of target data into a classification model, to obtain a plurality of corresponding classification results;
• and
• obtaining a target classification result based on the plurality of classification results, wherein the target classification result is used as a classification result of the to-be-classified data.

A classification model training apparatus, comprising:

• an obtaining module, configured to obtain a plurality of pieces of original training data, wherein a category label corresponding to each of the plurality of pieces of original training data is user-generated data or artificially intelligence (AI)-generated data;
• and
• a processing module, configured to obtain a plurality of pieces of target training data based on the plurality of pieces of original training data, wherein a first part of data of the plurality of pieces of target training data belongs to the plurality of pieces of original training data, each piece of target training data in a second part of data of the plurality of pieces of target training data is obtained based on corresponding original training data in the plurality of pieces of original training data, and a data amount of each piece of target training data is less than a data amount of the corresponding original training data, wherein
• the processing module is further configured to input the plurality of pieces of target training data into a classification model, to obtain a plurality of classification results corresponding to the plurality of pieces of target training data;
• and
• the processing module is further configured to update a first model based on a loss function value, to obtain a second model, wherein the loss function value is obtained based on the plurality of classification results and category labels corresponding to the plurality of pieces of target training data, a part of target training data in the second part of data does not have a category label, a data amount of the part of target training data is less than a preset threshold, and corresponding original training data of the part of target training data is AI-generated.

A classification apparatus, comprising:

• an obtaining module, configured to obtain to-be-classified data;
• and
• a processing module, configured to generate at least one piece of target data based on the to-be-classified data, wherein the at least one piece of target data is obtained based on the to-be-classified data, and a data amount of the at least one piece of target data is less than a data amount of the to-be-classified data, wherein
• the processing module is further configured to input the to-be-classified data and the at least one piece of target data into a classification model, to obtain a plurality of corresponding classification results;
• and
• the processing module is further configured to obtain a target classification result based on the plurality of classification results, wherein the target classification result is used as a classification result of the to-be-classified data.