公开(公告)号：US20230237309A1

公开(公告)日：2023-07-27

申请号：US18180841

申请日：2023-03-08

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Xiaoyun Zhou ,  Jiacheng Sun ,  Nanyang Ye ,  Xu Lan ,  Qijun Luo ,  Pedro Esperanca ,  Fabio Maria Carlucci ,  Zewei Chen ,  Zhenguo Li

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: A device for machine learning is provided, including a first neural network layer, a second neural network layer with a normalization layer arranged in between. The normalization layer is configured to, when the device is undergoing training on a batch of training samples, receive multiple outputs of the first neural network layer for a plurality of training samples of the batch, each output comprising multiple data values for different indices on a first dimension and a second dimension; group the outputs into multiple groups based on the indices on the first and second dimensions; form a normalization output for each group which are provided as input to the second neural network layer. According to the application, the training of a deep convolutional neural network with good performance that performs stably at different batch sizes and is generalizable to multiple vision tasks is achieved, thereby improving the performance of the training.

公开(公告)号：US20250094818A1

公开(公告)日：2025-03-20

申请号：US18965817

申请日：2024-12-02

Applicant: HUAWEI TECHNOLOGIES CO., LTD. ,  TSINGHUA UNIVERSITY

Inventor： Jun Zhu ,  Fan Bao ,  Chongxuan Li ,  Jiacheng Sun

IPC: G06N3/094 ,  G06F18/2135

Abstract: A data denoising method and a related device are provided. According to the method, an artificial intelligence technology may be used to perform denoising on data, and any target denoising operation in at least one denoising operation performed on noisy data includes: generating, based on first prediction information and second prediction information, distribution information corresponding to the target denoising operation, where the first prediction information indicates predicted noise between second noisy data and clean data, the second prediction information indicates a square of the predicted noise between the second noisy data and the clean data or indicates a square of a predicted distance between the first prediction information and actual noise, and the actual noise includes actual noise between the second noisy data and the clean data; and sampling denoised data in distribution space to which the distribution information points.