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：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.