公开(公告)号：US20220222525A1

公开(公告)日：2022-07-14

申请号：US17554870

申请日：2021-12-17

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Su Woong LEE ,  Seungjae LEE ,  Jong-Gook KO ,  Wonyoung YOO ,  Jung Jae YU ,  Keun Dong LEE ,  Yongsik LEE ,  Da-Un JUNG

IPC: G06N3/08 ,  G06K9/62

Abstract: Provided are a method and system for training a dynamic deep neural network. The method for training a dynamic deep neural network includes receiving an output of a last layer of the deep neural network and outputting a first loss, receiving an output of a routing module according to an input class of the deep neural network and outputting a second loss, calculating a third loss based on the first loss and the second loss, and updating a weight of the deep neural network by using the third loss.

公开(公告)号：US20210350241A1

公开(公告)日：2021-11-11

申请号：US17242604

申请日：2021-04-28

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Seungjae LEE ,  Jong-Gook KO ,  Keun Dong LEE ,  Su Woong LEE ,  Yongsik LEE ,  Da-Un JUNG ,  Wonyoung YOO

IPC: G06N3/08 ,  G06N3/04

Abstract: An apparatus and method for searching a neural network architecture may be disclosed. The apparatus may include an architecture searcher and an architecture evaluator. The architecture searcher may search for a topology between nodes included in a basic cell of a network, search for an operation to be applied between the nodes after searching for the topology, and determine the basic cell. The architecture evaluator may evaluate performance of the determined basic cell.

公开(公告)号：US20230334321A1

公开(公告)日：2023-10-19

申请号：US17976655

申请日：2022-10-28

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Su Woong LEE ,  Jong-Gook KO ,  Wonyoung YOO ,  Seungjae LEE ,  Yongsik LEE ,  Juwon LEE ,  Da-Un JUNG

IPC: G06N3/08

CPC classification number: G06N3/082 ,  G06N3/084

Abstract: Disclosed are a deep neural network lightweight device based on batch normalization, and a method thereof. The deep neural network lightweight device based on batch normalization includes a memory that stores at least one data and at least one processor that executes a network lightweight module. When executing the network lightweight module, the processor performs learning on an input neural network based on sparsity regularization to adaptively determine at least one parameter of the sparsity regularization, performs pruning on the learning result, and performs fine tuning on the pruning result.

公开(公告)号：US20160275124A1

公开(公告)日：2016-09-22

申请号：US15074252

申请日：2016-03-18

Applicant: Electronics and Telecommunications Research Institute

Inventor： Seungjae LEE ,  Keun Dong LEE ,  Weon Geun OH ,  DA-UN JUNG ,  Sungkwan JE

IPC: G06F17/30

CPC classification number: G06F16/29 ,  G06F16/5838

Abstract: Provided is a visual search system. The visual search system according to an embodiment of the inventive concept may include a database that stores characteristic information for a visual search, and a visual search update server that updates the DB based on an image of a target captured by a moving object or a fixed object. According to an embodiment of the inventive concept, it is possible to efficiently update a DB for a visual search because images captured by not only the moving object but also the fixed object are used.

Abstract translation: 提供了一种视觉搜索系统。 根据本发明构思的实施例的视觉搜索系统可以包括存储用于视觉搜索的特征信息的数据库，以及基于由移动对象捕获的目标或固定的目标的图像来更新DB的视觉搜索更新服务器 目的。 根据本发明构思的实施例，可以有效地更新用于视觉搜索的数据库，因为不仅使用移动对象而且使用固定对象捕获的图像。

公开(公告)号：US20150242703A1

公开(公告)日：2015-08-27

申请号：US14265283

申请日：2014-04-29

Applicant: Electronics and Telecommunications Research Institute

Inventor： Seungjae LEE ,  Sangil NA ,  Keun Dong LEE ,  Sungkwan JE ,  DA-UN JUNG ,  Weon Geun OH ,  Young Ho SUH ,  Wookho SON

IPC: G06K9/46 ,  G06F17/30

CPC classification number: G06K9/4671 ,  G06F17/30247

Abstract: A method for extracting features from an image for use in a computing device, the method comprising: producing Gaussian Scale Space (GSS) images in the type of a pyramid from the image inputted to the computing device; performing a Scale Normalized Laplacian Filtering on the GSS images; detecting interest points from the images that are subject to the Scale Normalized Laplacian Filtering; and extracting features of the image using the detected interest points.

Abstract translation: 一种用于从用于计算设备的图像中提取特征的方法，所述方法包括：从输入到所述计算设备的图像生成金字塔类型的高斯比例空间（GSS）图像; 对GSS图像执行缩放归一化拉普拉斯滤波; 从经受缩放归一化拉普拉斯滤波的图像中检测兴趣点; 以及使用检测到的兴趣点提取图像的特征。