公开(公告)号：US20220180199A1

公开(公告)日：2022-06-09

申请号：US17680630

申请日：2022-02-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yixing XU ,  Hanting CHEN ,  Kai HAN ,  Yunhe WANG ,  Chunjing XU

IPC: G06N3/08 ,  G06N3/04

Abstract: This application provides a neural network model compression method in the field of artificial intelligence. The method includes: obtaining, by a server, a first neural network model and training data of the first neural network that are uploaded by user equipment; obtaining a PU classifier based on the training data of the first neural network and unlabeled data stored in the server; selecting, by using the PU classifier, extended data from the unlabeled data stored in the server, where the extended data has a property and distribution similar to a property and distribution of the training data of the first neural network model; and training a second neural network model by using a knowledge distillation (KD) method based on the extended data, where the first neural network model is used as a teacher network model and the second neural network model is used as a student network model.

公开(公告)号：US20220019855A1

公开(公告)日：2022-01-20

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: G06K9/62 ,  G06N3/04

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：US20230419646A1

公开(公告)日：2023-12-28

申请号：US18237995

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Kai HAN ,  Yunhe WANG ,  An XIAO ,  Jianyuan GUO ,  Chunjing XU ,  Li QIAN

IPC: G06V10/80 ,  G06V10/40 ,  G06V10/82

CPC classification number: G06V10/806 ,  G06V10/40 ,  G06V10/82

Abstract: Embodiments of this disclosure relate to the field of artificial intelligence, and disclose a feature extraction method and apparatus. The method includes: obtaining a to-be-processed object, and obtaining a segmented object based on the to-be-processed object, where the segmented object includes some elements in the to-be-processed object, a first vector indicates the segmented object, and a second vector indicates some elements in the segmented object; performing feature extraction on the first vector to obtain a first feature, and performing feature extraction on the second vector to obtain a second feature; fusing at least two second features based on a first target weight, to obtain a first fused feature; and performing fusion processing on the first feature and the first fused feature to obtain a second fused feature, where the second fused feature is used to obtain a feature of the to-be-processed object.

公开(公告)号：US20210312261A1

公开(公告)日：2021-10-07

申请号：US17220158

申请日：2021-04-01

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yixing XU ,  Kai HAN ,  Yunhe WANG ,  Chunjing XU ,  Qi TIAN

IPC: G06N3/04 ,  G06N3/08

Abstract: The present application discloses a neural network search method in the field of artificial intelligence, and the neural network search method includes: obtaining a feature tensor of each of a plurality of neural networks, where the feature tensor of each neural network is used to represent a computing capability of the neural network; inputting the feature tensor of each of the plurality of neural networks into an accuracy prediction model for calculation, to obtain accuracy of each neural network, where the accuracy prediction model is obtained through training based on a ranking-based loss function; and determining a neural network corresponding to the maximum accuracy as a target neural network. Embodiments of the present invention help improve accuracy of a network structure found through search.

公开(公告)号：US20230401826A1

公开(公告)日：2023-12-14

申请号：US18456312

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jianyuan GUO ,  Kai HAN ,  Yunhe WANG ,  Chunjing XU

IPC: G06V10/77 ,  G06V10/82 ,  G06V10/80

CPC classification number: G06V10/7715 ,  G06V10/82 ,  G06V10/806

Abstract: This disclosure discloses a perception network. The perception network may be applied to the artificial intelligence field, and includes a feature extraction network. A first block in the feature extraction network is configured to perform convolution processing on input data, to obtain M target feature maps; at least one second block in the feature extraction network is configured to perform convolution processing on M1 target feature maps in the M target feature maps, to obtain M1 first feature maps; a target operation in the feature extraction network is used to process M2 target feature maps in the M target feature maps, to obtain M2 second feature maps; and a concatenation operation in the feature extraction network is used to concatenate the M1 first feature maps and the M2 second feature maps, to obtain a concatenated feature map.

公开(公告)号：US20220157041A1

公开(公告)日：2022-05-19

申请号：US17587689

申请日：2022-01-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Kai HAN ,  Yunhe WANG ,  Han SHU ,  Chunjing XU

IPC: G06V10/44 ,  G06V10/764 ,  G06V10/82

Abstract: This application relates to an image recognition technology in the field of computer vision in the field of artificial intelligence, and provides an image classification method and apparatus. The method includes: obtaining an input feature map of a to-be-processed image; performing convolution processing on the input feature map based on M convolution kernels of a neural network, to obtain a candidate output feature map of M channels, where M is a positive integer; performing matrix transformation on the M channels of the candidate output feature map based on N matrices, to obtain an output feature map of N channels, where a quantity of channels of each of the N matrices is less than M, N is greater than M, and N is a positive integer; and classify the to-be-processed image based on the output feature map, to obtain a classification result of the to-be-processed image.

公开(公告)号：US20250095352A1

公开(公告)日：2025-03-20

申请号：US18962726

申请日：2024-11-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Kai HAN ,  Jianyuan GUO ,  Yehui TANG ,  Yunhe WANG

IPC: G06V10/82 ,  G06V10/77 ,  G06V10/80

Abstract: This application discloses a visual task processing method and a related device thereof. A to-be-processed image can be processed using a target model, and features outputted by the target model can remain diversified, to help improve processing precision of a visual task for the to-be-processed image. The method in this application includes: obtaining a to-be-processed image; processing the to-be-processed image using a target model, to obtain a feature of the to-be-processed image, where the target model includes a first module and a second module connected to the first module, the first module includes a graph neural network, and the second module is configured to implement feature transformation; and completing a visual task for the to-be-processed image based on the feature of the to-be-processed image.

公开(公告)号：US20230351163A1

公开(公告)日：2023-11-02

申请号：US17733758

申请日：2022-04-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yehui TANG ,  Kai HAN ,  Jianyuan GUO ,  Yunhe WANG ,  Yanxi LI ,  Chang XU ,  Chao XU

IPC: G06N3/04 ,  G06K9/62

CPC classification number: G06N3/0481 ,  G06K9/6232 ,  G06K9/6261

Abstract: A method is provided for data processing based on a multi-layer perceptrons (MLP) architecture. The method comprises determining a plurality of tokens for a piece of data, generating an amplitude and a phase for each of the plurality of tokens, optimizing the plurality of tokens by mixing the plurality of tokens based on the amplitudes and the phases, and determining one or more features included in the piece of data based on the plurality of optimized tokens. Each token includes information associated with a segment of the piece of data.

公开(公告)号：US20200302265A1

公开(公告)日：2020-09-24

申请号：US16359346

申请日：2019-03-20

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yunhe WANG ,  Chunjing XU ,  Kai HAN

IPC: G06N3/04 ,  G06F17/16 ,  G06F17/15

Abstract: This application discloses a convolutional neural network-based image processing method and image processing apparatus in the artificial intelligence field. The method may include: receiving an input image; preprocessing the input image to obtain preprocessed image information; and performing convolution on the image information using a convolutional neural network, and outputting a convolution result. In embodiments of this application, the image processing apparatus may store primary convolution kernels of convolution layers, and before performing convolution using the convolution layers, generate secondary convolution kernels using the primary convolution kernels of the convolution layers.