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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.24 No.4
- 발행연도
- 2024.12
- 수록면
- 317 - 332 (16page)
- DOI
- 10.5391/IJFIS.2024.24.4.317
이용수
초록· 키워드
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Current deep learning models underperform when using loss functions characterized by single properties. Therefore, optimizing these models with a combination of multiple attributes is essential to enhance performance. We propose a novel hybrid nonlinear loss function technique incorporating heterogeneous nonlinear properties to achieve optimal performance. We evaluated the proposed method using six analytical techniques: contour map visualization, loss error frequency analysis, scatter plot visualization, loss function visualization, gradient descent analysis, and covariate Analysis with convex and linear coefficients. Our experiments on semantic segmentation tasks using the Pascal visual object classes and automatic target recognition datasets demonstrated superior optimization performance compared to existing loss functions.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Method
4. Experiment Method
5. Experiment Result
6. Conclusion
References
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