公开(公告)号：US12254064B2

公开(公告)日：2025-03-18

申请号：US17488735

申请日：2021-09-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hanting Chen ,  Yunhe Wang ,  Chuanjian Liu ,  Kai Han ,  Chunjing Xu

IPC: G06F18/214 ,  G06F18/243 ,  G06N3/047

Abstract: The present application discloses an image generation method, a neural network compression method, and a related apparatus and device in the field of artificial intelligence. The image generation method includes: inputting a first matrix into an initial image generator to obtain a generated image; inputting the generated image into a preset discriminator to obtain a determining result, where the preset discriminator is obtained through training based on a real image and a category corresponding to the real image; updating the initial image generator based on the determining result to obtain a target image generator; and further inputting a second matrix into the target image generator to obtain a sample image. Further, a neural network compression method is disclosed, to compress the preset discriminator based on the sample image obtained by using the foregoing image generation method.

公开(公告)号：US12131521B2

公开(公告)日：2024-10-29

申请号：US17587284

申请日：2022-01-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Hanting Chen ,  Yunhe Wang ,  Chunjing Xu

IPC: G06V10/764 ,  G06F17/16 ,  G06N3/02 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06F17/16 ,  G06N3/02 ,  G06V10/7715 ,  G06V10/82

Abstract: This application relates to an image recognition technology in the field of computer vision of artificial intelligence, and provides an image classification method and apparatus. An example method includes obtaining an input feature map of a to-be-processed image, and then performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map, where each of a plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and a difference between the target matrix and the input sub-feature map corresponding to the target matrix is the feature extraction kernel. The to-be-processed image is classified based on the output feature map to obtain a classification result of the to-be-processed image.