Method for training model, electronic device and storage medium

An electronic device comprising:

• a storage device;
• at least one processor;
• and
• the storage device storing one or more programs, which when executed by the at least one processor, cause the at least one processor to:
• obtain an image set used for training a model, the image set comprises a labeled image, an unlabeled image and a category label of the labeled image;
• discriminate the labeled image and the unlabeled image by a target sub-model of the model, and determine first classification reference information of the labeled image and first classification reference information of the unlabeled image;
• discriminate the unlabeled image by a non-target sub-model of the model, and determine second classification reference information of the unlabeled image;
• determine a classification loss of the target sub-model based on the first classification reference information of the labeled image, the category label of the labeled image, and the second classification reference information of the unlabeled image;
• and
• tune a model parameter of the model according to the classification loss.

A non-transitory storage medium having instructions stored thereon, when the instructions are executed by a processor of an electronic device, the processor is caused to perform a method for training a model, wherein the method comprises:

• obtaining an image set used for training a model, the image set comprises a labeled image, an unlabeled image and a category label of the labeled image;
• discriminating the labeled image and the unlabeled image by a target sub-model of the model, and determining first classification reference information of the labeled image and first classification reference information of the unlabeled image;
• discriminating the unlabeled image by a non-target sub-model of the model, and determining second classification reference information of the unlabeled image;
• determining a classification loss of the target sub-model based on the first classification reference information of the labeled image, the category label of the labeled image, and the second classification reference information of the unlabeled image;
• and
• tuning a model parameter of the model according to the classification loss.