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 Eo ; Jihwan Choi ; Sunghoon Im
- Advisor
- 최지환
- Co-Advisor(s)
- Sunghoon Im
- Issued Date
- 2021
- Awarded Date
- 2021/02
- Type
- Thesis
- 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
- Degree
- Master
- Department
- Information and Communication Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Im, Sunghoon
- Research Interests Computer Vision; Deep Learning; Robot Vision
-
- Files in This Item:
-
There are no files associated with this item.
- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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