公开(公告)号：US20230082597A1

公开(公告)日：2023-03-16

申请号：US17990125

申请日：2022-11-18

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yunfeng Lin ,  Guilin Li ,  Xing Zhang ,  Weinan Zhang ,  Zhenguo Li

IPC: G06N3/04 ,  G06N3/08

Abstract: A neural network construction method and system in the field of artificial intelligence, to construct a target neural network by replacing a part of basic units in an initial backbone network with placeholder modules, so that different target neural networks can be constructed based on different scenarios. The method may include obtaining an initial backbone network and a candidate set, replacing at least one basic unit in the initial backbone network with at least one placeholder module to obtain a to-be-determined network, performing sampling based on the candidate set to obtain information about at least one sampling structure, and obtaining a network model based on the to-be-determined network and the information about the at least one sampling structure. The information about the at least one sampling structure may be used for determining a structure of the at least one placeholder module.

公开(公告)号：US20210197855A1

公开(公告)日：2021-07-01

申请号：US17198937

申请日：2021-03-11

Applicant: Huawei Technologies Co., Ltd.

Inventor： Xing Zhang ,  Lin Lan ,  Zhenguo Li ,  Li Qian

IPC: B60W60/00 ,  G06N3/08

Abstract: A self-driving method and a related apparatus, the method including determining, by a self-driving apparatus, a task feature vector of a self-driving task according to M groups of historical paths of the self-driving task, where the task feature vector is a vector representing features of the self-driving task, and where M is an integer greater than 0, determining, by the self-driving apparatus, according to the task feature vector and a status vector, a target driving operation that needs to be performed, where the status vector indicates a driving status of the self-driving apparatus, and performing, by the self-driving apparatus, the target driving operation.

公开(公告)号：US10997778B2

公开(公告)日：2021-05-04

申请号：US16464202

申请日：2017-02-23

Applicant: Huawei Technologies Co., Ltd.

Inventor： Xing Zhang ,  Kun Liu ,  Jiangwei Li ,  Cheng Du

IPC: G06T15/20 ,  G06T11/00 ,  G06T17/20

Abstract: A method, where one file includes file data of a plurality of files for generating a three-dimensional model includes obtaining a first file in a picture format, an extension data segment of the first file includes at least one first data segment, and the first data segment includes the file data of the files for generating a first three-dimensional model of the three-dimensional model, obtaining the file data of the files from the first data segment, and generating the first three-dimensional model based on the file data of the files.

公开(公告)号：US20230206069A1

公开(公告)日：2023-06-29

申请号：US18175936

申请日：2023-02-28

Applicant: Huawei Technologies Co., Ltd.

Inventor： Junlei Zhang ,  Chuanjian Liu ,  Guilin Li ,  Xing Zhang ,  Wei Zhang ,  Zhenguo Li

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: A deep learning training method includes obtaining a training set, a first neural network, and a second neural network, where shortcut connections included in the first neural network are less than shortcut connections included in the second neural network; performing at least one time of iterative training on the first neural network based on the training set, to obtain a trained first neural network, where any iterative training includes: using a first output of at least one first intermediate layer in the first neural network as an input of at least one network layer in the second neural network, to obtain an output result of the at least one network layer; and updating the first neural network according to a first loss function.