公开(公告)号：US20230419171A1

公开(公告)日：2023-12-28

申请号：US17956170

申请日：2022-09-29

Applicant: Northwestern Polytechnical University

Inventor： Yunji Liang ,  Qiushi Wang ,  Hangyu Hu ,  Zhiying Zhao ,  Lei Liu

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: The invention discloses an energy-efficient sample selection method based on sample complexity, which performs sample selection on the raw data sets through two stages of inter-class sampling and intra-class sampling, the object is to select representative samples from large-scale data sets, thereby reducing the number of samples used for model training and achieving the object of lightweight training. Compared with the prior art, the invention has the following advantages: the invention proposes an energy-efficient sample selection method based on complexity, selects representative samples from large-scale datasets for efficient model training, and proves that sample complexity and model training strategies have a very important impact on the efficient training of deep neural networks. The invention also solves the problem of low efficiency of model training based on sample complexity and model training strategies, which has certain significance for alleviating the problem of low efficiency of deep learning model training.