公开(公告)号：US20240281930A1

公开(公告)日：2024-08-22

申请号：US18571116

申请日：2022-09-19

Applicant: Beijing Zitiao Network Technology Co., Ltd.

Inventor： Jie WU ,  Shaojie LI ,  Xuefeng XIAO

IPC: G06T5/20 ,  G06V10/74 ,  G06V10/771

CPC classification number: G06T5/20 ,  G06V10/761 ,  G06V10/771

Abstract: Embodiments of the present disclosure provide a network model compression method, apparatus and device, an image generation method, and a medium. The network model compression method includes: performing pruning processing on the first generator to obtain a second generator; and configuring states of convolution kernels in the first discriminator to enable a part of the convolution kernels to be in an activated state and the other part of the convolution kernels to be in a suppressed state, so as to obtain a second discriminator. A loss difference between the first generator and the first discriminator is a first loss difference, a loss difference between the second generator and the second discriminator is a second loss difference, and an absolute value of a difference value between the first loss difference and the second loss difference is less than a first preset threshold.

公开(公告)号：US20250078355A1

公开(公告)日：2025-03-06

申请号：US18725707

申请日：2023-07-18

Applicant: Beijing Zitiao Network Technology Co., Ltd.

Inventor： Yuxi REN ,  Jie WU ,  Peng ZHANG ,  Xuefeng XIAO

IPC: G06T11/60

Abstract: Embodiments of the present disclosure provide an image processing method and apparatus, an electronic device, and a storage medium, and the method includes: acquiring an original image to be processed; inputting the original image into a first image processing model; processing the original image by the first image processing model to generate a target image, in which the first image processing model and a second image processing model are obtained by online alternate training, supervision information during training process of the first image processing model includes at least part of images generated by the second image processing model during training process, and a model scale of the first image processing model is smaller than a model scale of the second image processing model; and outputting the target image.