Patent ID: 11908239
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 10:
11. A model training apparatus, comprising:
at least one memory configured to store program code; and
at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
first acquisition code configured to cause at least one of the at least one processor to acquire a first image feature corresponding to an image set, the image set comprising at least one image, an image of the at least one image having an identity tag and a pose tag, the identity tag representing identity information of a target object in the image, and the pose tag representing pose information of the target object in the image;
second acquisition code configured to cause at least one of the at least one processor to acquire, by using an identity classifier, a first identity prediction result corresponding to the first image feature, and acquire, by using a pose classifier, a first pose prediction result corresponding to the first image feature;
first training code configured to cause at least one of the at least one processor to train the identity classifier according to the first identity prediction result and the identity tag, and train the pose classifier according to the first pose prediction result and the pose tag;
transformation code configured to cause at least one of the at least one processor to perform, by using a generator, pose transformation on the first image feature, to obtain a second image feature corresponding to the image set, the second image feature being corresponding to a target pose;
third acquisition code configured to cause at least one of the at least one processor to acquire, by using the trained identity classifier, a second identity prediction result corresponding to the second image feature, and acquire, by using the trained pose classifier, a second pose prediction result corresponding to the second image feature; and
second training code configured to cause at least one of the at least one processor to train the generator according to the second identity prediction result, the identity tag, the second pose prediction result, a target pose tag, the second image feature, and a third image feature, the third image feature being an image feature corresponding to an image belonging to the target pose in the image set, the target pose tag representing information of the target pose, and the generator being configured to generate an image recognition network model,
wherein the second training code comprises:
first minimum loss result determination code configured to cause at least one of the at least one processor to determine a first minimum loss result according to the second identity prediction result and the identity tag by using a third loss function;
second minimum loss result determination code configured to cause at least one of the at least one processor to determine a second minimum loss result according to the second pose prediction result and the target pose tag by using a fourth loss function;
third minimum loss result determination code configured to cause at least one of the at least one processor to determine a third minimum loss result according to the second image feature and the third image feature by using a fifth loss function, the second image feature being a false input of a discriminator, the third image feature being a true input of the discriminator, and the discriminator being configured to discriminate an authenticity of the second image feature and the third image feature;
fourth minimum loss result determination code configured to cause at least one of the at least one processor to determine a fourth minimum loss result according to the third image feature by using a sixth loss function; and
generator training code configured to cause at least one of the at least one processor to train the generator according to the first minimum loss result, the second minimum loss result, the third minimum loss result, and the fourth minimum loss result.