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1. CN112434746 - Pre-labeling method based on hierarchical transfer learning and related equipment thereof

Office
Chine
Numéro de la demande 202011364408.9
Date de la demande 27.11.2020
Numéro de publication 112434746
Date de publication 02.03.2021
Type de publication A
CIB
G06K 9/62
GPHYSIQUE
06CALCUL; COMPTAGE
KRECONNAISSANCE DES DONNÉES; PRÉSENTATION DES DONNÉES; SUPPORTS D'ENREGISTREMENT; MANIPULATION DES SUPPORTS D'ENREGISTREMENT
9Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
62Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06N 20/00
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
20Apprentissage automatique
CPC
G06K 9/6223
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6218Clustering techniques
622Non-hierarchical partitioning techniques
6221based on statistics
6223with a fixed number of clusters, e.g. K-means clustering
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
Déposants PING AN TECHNOLOGY (SHENZHEN) CO., LTD.
平安科技(深圳)有限公司
Inventeurs ZHANG NAN
张楠
WANG JIANZONG
王健宗
QU XIAOYANG
瞿晓阳
Mandataires 深圳市世联合知识产权代理有限公司 44385
Titre
(EN) Pre-labeling method based on hierarchical transfer learning and related equipment thereof
(ZH) 基于层次化迁移学习的预标注方法及其相关设备
Abrégé
(EN) The embodiment of the invention belongs to the field of artificial intelligence, and relates to a pre-labeling method based on hierarchical transfer learning and related equipment thereof, and the method comprises the steps: carrying out the clustering of a plurality of different scenes received in advance based on a preset clustering algorithm, and obtaining a clustering result; determining a first type of scenes and a second type of scenes according to the clustering result, the first type of scenes including the first scene, and the data volume of the annotation data in the first scene being greater than the data volume of the annotation data of any scene in the second type of scenes; performing transfer learning on the first type of scenes based on a preset identification model to obtain pre-annotation data and a transfer model of each scene in the first type of scenes; and based on a migration model, carrying out migration learning on the second type of scenes to obtain pre-annotation data of each scene in the second type of scenes. The pre-annotation data of each scene can be stored in the block chain. According to the method and the device, better pre-annotation data in different scenes can be quickly obtained.
(ZH) 本申请实施例属于人工智能领域,涉及一种基于层次化迁移学习的预标注方法及其相关设备,包括基于预设的聚类算法对预先接收的多个不同的场景进行聚类,获得聚类结果;根据聚类结果确定第一类场景和第二类场景,第一类场景包括第一场景,第一场景中标注数据的数据量大于第二类场景中任意场景的标注数据的数据量;基于预设的识别模型对所述第一类场景进行迁移学习,获得第一类场景中每个场景的预标注数据和迁移模型;基于迁移模型对所述第二类场景进行迁移学习,获得第二类场景中每个场景的预标注数据。其中,每个场景的预标注数据可存储于区块链中。本申请实现快速获得不同场景中较好的预标注数据。
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