公开(公告)号：US11328180B2

公开(公告)日：2022-05-10

申请号：US16666631

申请日：2019-10-29

Applicant: Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Inventor： Yonghao Xu ,  Qian Zhang ,  Guoli Wang ,  Chang Huang

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: Disclosed are a method for updating a neural network and an electronic device. The method includes: inputting a first image set having tag information into a first depth neural network, and determining a cross entropy loss value of the first image set by using the first depth neural network; inputting a second image set having no tag information separately into the first depth neural network and a second depth neural network, and determining a consistency loss value of the second image set, the first depth neural network and the second depth neural network having the same network structure; updating parameters of the first depth neural network based on the cross entropy loss value and the consistency loss value; and updating parameters of the second depth neural network based on the updated parameters of the first depth neural network.

公开(公告)号：US12093810B2

公开(公告)日：2024-09-17

申请号：US17290519

申请日：2019-11-04

Applicant: BEIJING HORIZON ROBOTICS TECHNOLOGY RESEARCH AND DEVELOPMENT CO., LTD.

Inventor： Haoqian He ,  Jianjun Li ,  Chang Huang

IPC: G06F3/06 ,  G06N3/04 ,  G06N3/063

CPC classification number: G06N3/063 ,  G06F3/0658 ,  G06N3/04

Abstract: Disclosed are a convolution processing engine and a control method thereof, and a convolutional neural network accelerator comprising the convolution processing engine. The convolution processing engine comprises at least two cache memories connected in series and an operational circuit. The convolution processing engine can realize an efficient convolution operation with lower complexity and power consumption.

公开(公告)号：US11195098B2

公开(公告)日：2021-12-07

申请号：US16666344

申请日：2019-10-28

Applicant: Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Inventor： Yukang Chen ,  Qian Zhang ,  Chang Huang

IPC: G06N3/08 ,  G06K9/62 ,  G06N20/20 ,  G06N3/04

Abstract: Disclosed are a method for generating a neural network, an apparatus thereof, and an electronic device. The method includes: obtaining an optimal neural network and a worst neural network from a neural network framework by using an evolutionary algorithm; obtaining an optimized neural network from the optimal neural network by using a reinforcement learning algorithm; updating the neural network framework by adding the optimized neural network into the neural network framework and deleting the worst neural network from the neural network framework; and determining an ultimately generated neural network from the updated neural network framework. In this way, a neural network is optimized and updated from a neural network framework by combining the evolutionary algorithm and the reinforcement learning algorithm, thereby automatically generating a neural network structure rapidly and stably.

公开(公告)号：US11037031B2

公开(公告)日：2021-06-15

申请号：US16788661

申请日：2020-02-12

Applicant: Beijing Horizon Robotics Technology Research and Development Co., Ltd.

Inventor： Chaoxu Guo ,  Qian Zhang ,  Guoli Wang ,  Chang Huang

IPC: G06K9/62 ,  G06N3/04

Abstract: An image recognition method includes: determining a first feature map of the current frame image by using a convolutional neural network based on a type of a current frame image; determining a second feature map of a key frame image before the current frame image; performing feature alignment on the first feature map and the second feature map to obtain a first aligned feature map; fusing the first feature map and the first aligned feature map to obtain a first fused feature map; and recognizing content in the current frame image based on the first fused feature map.