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dc.contributor.advisor 최지환 -
dc.contributor.author Jaemyeong Eo -
dc.date.accessioned 2022-07-07T02:29:24Z -
dc.date.available 2022-07-07T02:29:24Z -
dc.date.issued 2021 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000368542 en_US
dc.identifier.uri http://hdl.handle.net/20.500.11750/16732 -
dc.description.statementofresponsibility N -
dc.description.tableofcontents Abstract i
List of contents ii
I. INTRODUCTION
1.1 Machine Learning Model Interpretability 1
1.2 Curriculum Learning 2
1.3 Contributions and Overview of Thesis 2
II. BATCH-WISE POST-HOC ANALYSIS FOR IMAGE CLASSIFICATION
2.1 Dataset 3
2.2 Neural Network Model and Experiment Environment 3
2.3 Experiment Design 4
2.4 Result Analysis 4
III. CONCLUSION AND FUTURE WORK
3.1 Concluding remarks 9
3.2 Future work 9
IV. Appendix
4.1 Python code 10
References 13
요약문 14
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dc.format.extent 14 -
dc.language eng -
dc.publisher DGIST -
dc.subject Deep Learning, Convolutional Neural Network, Curriculum Learning, Model Interpretability, 심층신경망, 설명 가능한 모델, 커리큘럼 학습 -
dc.title Post-hoc Analysis of Trained Classification Models for Interpretability -
dc.title.alternative 설명 가능한 심층신경망 모델학습 방법론 분석 -
dc.type Thesis -
dc.identifier.doi 10.22677/thesis.200000368542 -
dc.description.degree Master -
dc.contributor.department Information and Communication Engineering -
dc.contributor.coadvisor Sunghoon Im -
dc.date.awarded 2021/02 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.IM 어73 202102 -
dc.contributor.alternativeDepartment 정보통신융합전공 -
dc.embargo.liftdate 2026-02-28 -
dc.contributor.affiliatedAuthor Jaemyeong Eo -
dc.contributor.affiliatedAuthor Jihwan Choi -
dc.contributor.affiliatedAuthor Sunghoon Im -
dc.contributor.alternativeName 어재명 -
dc.contributor.alternativeName Jihwan Choi -
dc.contributor.alternativeName 임성훈 -
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Department of Electrical Engineering and Computer Science Theses Master

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