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1. KR1020190140824 - 트리플릿 기반의 손실함수를 활용한 순서가 있는 분류문제를 위한 딥러닝 모델 학습 방법 및 장치

Office
Republic of Korea
Application Number 1020190043019
Application Date 12.04.2019
Publication Number 1020190140824
Publication Date 20.12.2019
Publication Kind A
IPC
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
CPC
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06K 9/6267
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
Applicants 한국과학기술원
Inventors 양현승
임우빈
홍성은
윤성의
Agents 특허법인충현
Priority Data 1020180062705 31.05.2018 KR
Title
(KO) 트리플릿 기반의 손실함수를 활용한 순서가 있는 분류문제를 위한 딥러닝 모델 학습 방법 및 장치
Abstract
(KO) 본 발명은 기계 학습을 이용한 영상 처리에 관한 기술로, 순서가 있는 분류 문제를 위한 딥러닝 모델을 학습하는 방법은, 학습 대상을 입력으로 하고 분기점과 그 분기에서 나누어져 분류 손실(classification loss)과 트리플릿 손실(triplet loss)을 발생시키는 두 개의 종단점으로 구성된 CNN(Convolutional Neural Networks)을 형성하고, 종단간(end-to-end) 학습을 위한 분류 손실을 산출하고, 네트워크가 순서 특성을 학습할 수 있도록 트리플릿 손실을 산출하며, 산출된 분류 손실 및 트리플릿 손실에 기반하되 상관 트리플릿 샘플링(relative triplet sampling)을 수행함으로써 최종 손실값에 대해 네트워크를 갱신함으로써, 효과적인 학습과 손실 제어가 가능하다.
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