Earticle

초록

영어

The advancement of deep learning has significantly improved image classification performance. However, the complexity of large-scale datasets continues to present challenges in terms of training time and resource consumption. One approach to address these issues is Self- Paced Curriculum Learning. Self-Paced Curriculum Learning begins by training on relatively easy data, allowing the model to autonomously select data and adjust difficulty levels as training progresses, gradually incorporating more complex data. This method improves the efficiency of the training process while minimizing performance degradation. In this study, we propose an approach that combines Self- Paced Curriculum Learning with the exclusion of low-noise data from the training process to further enhance training speed. The experimental results show that reducing the amount of data improves training speed. However, accuracy tends to decrease as the extent of data reduction increases.

목차

Abstract
I. INTRODUCTION
II. METHOD
A. Related work
B. SPCL with Data Reduction
III. EXPERIMENTAL RESULTS
IV. CONCLUSION
REFERENCES

키워드

저자

• Mi-Young Choi [ School of Computing Gachon University Seongnam, Repulic of Korea ]
• Sang-Woong Lee [ School of Computing Gachon University Seongnam, Repulic of Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004