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REFGAN : understanding deep network decisions via reference generation

REFGAN : understanding deep network decisions via reference generation

Material type
학위논문
Personal Author
박주연, 朴珠延
Title Statement
REFGAN : understanding deep network decisions via reference generation / Jouyon Park
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2020  
Physical Medium
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  
General Note
지도교수: 이성환  
Bibliography, Etc. Note
참고문헌: 장 21-22
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Computer Science,,
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007 ta
008 200107s2020 ulkad bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
041 0 ▼a eng ▼b kor
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6D36 ▼c 1113
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

Electronic Information

No. Title Service
1
REFGAN : understanding deep network decisions via reference generation (14회 열람)
View PDF Abstract Table of Contents

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1113 Accession No. 123063747 Availability Available Due Date Make a Reservation Service B M
No. 2 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6D36 1113 Accession No. 123063748 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

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.

Table of Contents

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                                                       

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