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REFGAN : understanding deep network decisions via reference generation

REFGAN : understanding deep network decisions via reference generation

자료유형
학위논문
개인저자
박주연, 朴珠延
서명 / 저자사항
REFGAN : understanding deep network decisions via reference generation / Jouyon Park
발행사항
Seoul :   Graduate School, Korea University,   2020  
형태사항
iv, 22장 : 천연색삽화, 도표 ; 26 cm
기타형태 저록
REFGAN: Understanding Deep Network Decisions via Reference Generation   (DCOLL211009)000000127348  
학위논문주기
학위논문(석사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2020. 2
학과코드
0510   6D36   1113  
일반주기
지도교수: 이성환  
서지주기
참고문헌: 장 21-22
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Computer Science,,
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100 1 ▼a 박주연, ▼g 朴珠延
245 1 0 ▼a REFGAN : ▼b understanding deep network decisions via reference generation / ▼d Jouyon Park
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020
300 ▼a iv, 22장 : ▼b 천연색삽화, 도표 ; ▼c 26 cm
500 ▼a 지도교수: 이성환
502 0 ▼a 학위논문(석사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2020. 2
504 ▼a 참고문헌: 장 21-22
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Computer Science
776 0 ▼t REFGAN: Understanding Deep Network Decisions via Reference Generation ▼w (DCOLL211009)000000127348
900 1 0 ▼a 이성환, ▼g 李晟瑍, ▼e 지도교수
900 1 0 ▼a Park, Jou-yon, ▼e
945 ▼a KLPA

전자정보

No. 원문명 서비스
1
REFGAN : understanding deep network decisions via reference generation (14회 열람)
PDF 초록 목차

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1113 등록번호 123063747 도서상태 대출가능 반납예정일 예약 서비스 B M
No. 2 소장처 과학도서관/학위논문서고/ 청구기호 0510 6D36 1113 등록번호 123063748 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

초록

Abstract
Due to the complex nature of machine learning models, it has been hard to understand how networks behave, and why they fail. Recent work on explaining deep network decisions provide visual hints which show which feature in the input image deep architectures focused on. However, these visual explanations only give rough semantic explanations such as high-lighting important spots or features. These explanations are often not sufficient for people to understand, and furthermore interact with machine learning models. In this paper, we propose a framework to explain deep network decisions using reference generation based on the training data distribution. We give rich example images which show the direct example data in the training set that influenced the classification process, and may be used to retrain the classifier when the user does not agree with the data point.

목차

1 Introduction  1
2 Related Works 6
3 Materials and Methods 9
3.1 Generative Adversarial Networks (GAN) 10
3.2 Searching Latent Space 12
3.3 Finding Reference Images 13
4 Results and Analysis 15                                                                                                              
4.1 Image Analysis 15
4.2 Classifier Analysis 17
4.3 Retraining Scheme 18
5 Discussion and Conclusions 20
6 Reference 21