公开(公告)号：US20240070436A1

公开(公告)日：2024-02-29

申请号：US17900592

申请日：2022-08-31

Applicant: Huawei Technologies Co., Ltd.

Inventor： Hang XU ,  Lu HOU ,  Guansong LU ,  Minzhe NIU ,  Zhenguo LI ,  Runhui HUANG ,  Lewei YAO ,  Chunjing XU ,  Xiaodan LIANG

IPC: G06N3/04 ,  G06F40/284

CPC classification number: G06N3/0454 ,  G06F40/284

Abstract: A method is provided for data processing performed by a processing system. The method comprises determining a set of first tokens for first data and a set of second token for second data, each token comprising information associated with a segment of the respective data, determining pair-wise similarities between the set of first tokens and the set of second tokens, each pair comprising a first token in the set of first tokens and a second token in the set of second tokens, determining, for each first token in the set of first tokens, a maximum similarity based on the determined pair-wise similarities between the respective first token and the second tokens in the set of second tokens, and determining a first similarity between the first data and the second data by aggregating the maximum similarities corresponding to the first tokens in the set of first set of tokens.

公开(公告)号：US20230401446A1

公开(公告)日：2023-12-14

申请号：US18238016

申请日：2023-08-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yehui TANG ,  Yixing XU ,  Yunhe WANG ,  Chunjing XU

IPC: G06N3/082 ,  G06N3/0464

CPC classification number: G06N3/082 ,  G06N3/0464 ,  G06V10/82

Abstract: Embodiments of this application disclose a convolutional neural network pruning processing method, a data processing method, and a device, which may be applied to the field of artificial intelligence. The convolutional neural network pruning processing method includes: performing sparse training on a convolutional neural network by using a constructed objective loss function, where the objective loss function may include three sub-loss functions.

公开(公告)号：US20230177641A1

公开(公告)日：2023-06-08

申请号：US18147371

申请日：2022-12-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Dehua SONG ,  Yunhe WANG ,  Hanting CHEN ,  Chunjing XU

IPC: G06T3/40 ,  G06T5/00 ,  G06T5/20

CPC classification number: G06T3/4046 ,  G06T3/4015 ,  G06T3/4053 ,  G06T5/003 ,  G06T5/20 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A neural network training method, includes: obtaining an input feature map of a training image; performing feature extraction processing on the input feature map by using a feature extraction core of a neural network to obtain a first candidate feature map; adding the first candidate feature map and a second candidate feature map to obtain an output feature map, where the second candidate feature map is a feature map obtained after a value corresponding to each element in the input feature map is increased by N times, and N is greater than 0; determining an image processing result of the training image based on the output feature map; and adjusting a parameter of the neural network based on the image processing result.

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：US20220157046A1

公开(公告)日：2022-05-19

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 ,  G06V10/77 ,  G06V10/82 ,  G06F17/16 ,  G06N3/02

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. The method includes: obtaining an input feature map of a to-be-processed image; 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; and classifying 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：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.