Patent ID: 11908102
Assignee: BOE TECHNOLOGY GROUP CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A training method of a neural network, comprising:
training a discriminative network based on a generative network to be trained;
training the generative network to be trained based on the discriminative network; and
alternately executing the above training processes to obtain a generative network,
wherein the generative network is configured to process an input image network to generate an output image, the generative network comprises a first sub-network and at least one second sub-network, the first sub-network is configured to process the input image to obtain a plurality of first feature images, the at least one second sub-network is configured to perform a branching process and a weight sharing process on the plurality of first feature images to obtain a plurality of second feature images, and the plurality of second feature images is processed to obtain the output image;
the training the generative network to be trained based on the discriminative network, comprises:
processing a first training input image by using the generative network to be trained to generate a first training output image;
calculating a system loss value of the generative network to be trained through a system loss function based on the first training output image; and
tuning parameters of the generative network to be trained based on the system loss value,

wherein the generative network further comprises a third sub-network, the plurality of second feature images is processed to obtain a plurality of third feature images; the plurality of third feature images is processed to obtain a plurality of fourth feature images by using the third sub-network; and the plurality of fourth feature images is performed through a synthesis process to obtain the output image,
wherein the third sub-network comprises a second backbone network, a fourth branch network, a fifth branch network, and a sixth branch network, the plurality of third feature images is processed to obtain a plurality of fifth feature images by using the second backbone network, the plurality of fifth feature images is divided into a fourth branch input, a fifth branch input, and a sixth branch input, the fourth branch input is processed by using the fourth branch network to obtain a fourth feature image corresponding to the fourth branch network, the fifth branch input is processed by using the fifth branch network to obtain a fourth feature image corresponding to the fifth branch network, and the sixth branch input is processed by using the sixth branch network to obtain a fourth feature image corresponding to the sixth branch network; and
wherein the plurality of fourth feature images comprises the fourth feature image corresponding to the fourth branch network, the fourth feature image corresponding to the fifth branch network and the fourth feature image corresponding to the sixth branch network.