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Post-hoc Analysis of Trained Classification Models for Interpretability

Title
Post-hoc Analysis of Trained Classification Models for Interpretability
Alternative Title
설명 가능한 심층신경망 모델학습 방법론 분석
Author(s)
Jaemyeong Eo
DGIST Authors
Jaemyeong EoJihwan ChoiSunghoon Im
Advisor
최지환
Co-Advisor(s)
Sunghoon Im
Issued Date
2021
Awarded Date
2021/02
Type
Thesis
Subject
Deep Learning, Convolutional Neural Network, Curriculum Learning, Model Interpretability, 심층신경망, 설명 가능한 모델, 커리큘럼 학습
Table Of Contents
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
URI
http://dgist.dcollection.net/common/orgView/200000368542

http://hdl.handle.net/20.500.11750/16732
DOI
10.22677/thesis.200000368542
Degree
Master
Department
Information and Communication Engineering
Publisher
DGIST
Related Researcher
  • 임성훈 Im, Sunghoon 전기전자컴퓨터공학과
  • Research Interests Computer Vision; Deep Learning; Robot Vision
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Department of Electrical Engineering and Computer Science Theses Master

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