公开(公告)号：US20250104406A1

公开(公告)日：2025-03-27

申请号：US18975854

申请日：2024-12-10

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Fengwei ZHOU ,  Chuanlong XIE ,  Qishi DONG ,  Tianyang HU ,  Yongxin YANG ,  Zhenguo LI

IPC: G06V10/776 ,  G06V10/70 ,  G06V10/77 ,  G06V10/774

Abstract: This application relates to a model migration method in the field of artificial intelligence, including: obtaining sample data of a target task, where the sample data includes a plurality of image samples; separately evaluating N pre-trained models based on the sample data, to obtain N evaluation values, where the evaluation value represents adaptation between the pre-trained model and the target task, and N≥2; determining K pre-trained models from the N pre-trained models based on the N evaluation values, where the K pre-trained models are models corresponding to first K evaluation values obtained by sorting the N evaluation values in descending order, and 1≤K≤N; and processing the sample data based on the K pre-trained models to obtain a target model used to process the target task, where the target model includes the K pre-trained models.

公开(公告)号：US20190286986A1

公开(公告)日：2019-09-19

申请号：US16431393

申请日：2019-06-04

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Wu BIN ,  Fengwei ZHOU ,  Zhenguo LI

IPC: G06N3/08

Abstract: Embodiments of the present invention provide a machine learning model training method, including: obtaining target task training data and N categories of support task training data; inputting the target task training data and the N categories of support task training data into a memory model to obtain target task training feature data and N categories of support task training feature data; training the target task model based on the target task training feature data and obtaining a first loss of the target task model, and separately training respectively corresponding support task models based on the N categories of support task training feature data and obtaining respective second losses of the N support task models; and updating the memory model, the target task model, and the N support task models based on the first loss and the respective second losses of the N support task models.