公开(公告)号：US20220215227A1

公开(公告)日：2022-07-07

申请号：US17704551

申请日：2022-03-25

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Guilin LI ,  Zhenguo LI ,  Xing ZHANG

IPC: G06N3/04 ,  G06N3/063

Abstract: This application provides a neural architecture search method, an image processing method and apparatus, and a storage medium. The method includes: determining a search space and a plurality of structuring elements, stacking the plurality of structuring elements to obtain an initial neural architecture at a first stage, and optimizing the initial neural architecture at the first stage to be convergent; and after an initial neural architecture optimized at the first stage is obtained, optimizing the initial neural architecture at a second stage to be convergent, to obtain optimized structuring elements, and building a target neural network based on the optimized structuring elements. Each edge of the initial neural architecture at the first stage and each edge of the initial neural architecture at the second stage correspond to a mixed operator including one type of operations and a mixed operator including a plurality of types of operations respectively.

公开(公告)号：US20220148291A1

公开(公告)日：2022-05-12

申请号：US17582880

申请日：2022-01-24

Applicant: Huawei Technologies Co., Ltd. ,  Peking University

Inventor： Weiran HUANG ,  Aoxue LI ,  Zhenguo LI ,  Tiange LUO ,  Liwei WANG

IPC: G06V10/774 ,  G06N3/04

Abstract: This application relates to an image recognition technology in the artificial intelligence field, and provides an image classification method and apparatus, and an image classification model training method and apparatus. This application relates to the artificial intelligence field, and more specifically, to the computer vision field. The method includes: obtaining a to-be-processed image; and classifying the to-be-processed image based on a preset global class feature, to obtain a classification result of the to-be-processed image. The global class feature includes a plurality of class features obtained through training based on a plurality of training images in a training set. The plurality of class features in the global class feature are used to indicate visual features of all classes in the training set.

公开(公告)号：US20210006760A1

公开(公告)日：2021-01-07

申请号：US17031423

申请日：2020-09-24

Applicant: Huawei Technologies Co., Ltd.

Inventor： Steven George MCDONAGH ,  Sarah PARISOT ,  Gregory SLABAUGH ,  Zhenguo LI

IPC: H04N9/73 ,  G06T7/90

Abstract: A processing entity generates a model for estimating scene illumination colour for a source image captured by a camera The processing entity acquires a set of images, captured by a respective camera, the set of images as a whole including images captured by multiple cameras; forms a set of tasks by assigning each image of the images set to a respective task such that images in the same task have in common that a the images are in a predetermined range; trains model parameters by repeatedly: selecting at least one of the tasks, forming an interim set of model parameters based on a first subset of the images of that task, estimating the quality of the interim set of model parameters against a second subset of the images of that task and updating the parameters of the model based on the interim set of parameters and the estimated quality.

公开(公告)号：US20190266191A1

公开(公告)日：2019-08-29

申请号：US16405367

申请日：2019-05-07

Applicant: Huawei Technologies Co., Ltd.

Inventor： Zhenguo LI ,  Jiefeng CHENG ,  Zhihong ZHAO

IPC: G06F16/901

Abstract: The method of the present disclosure includes: after a graph partitioning apparatus extracts an edge, first determining whether an aggregation degree between a currently extracted edge and an allocated edge in a first device satisfies a preset condition; then, when the preset condition is satisfied, determining whether a quantity of allocated edges stored in the first device is less than a first preset threshold; and allocating the currently extracted edge to the first device when the quantity is less than the first preset threshold. In this way, an aggregation degree between allocated edges in each device is relatively high and each device has relatively balanced load. When an edge changes and an edge associated with the particular edge needs to be synchronized, a relatively small quantity of devices need to perform synchronization and update, so that costs of communication between devices are reduced, and distributed graph computing efficiency is improved.