Earticle

초록

영어

Semi-supervised learning (SSL) has shown great promise in utilizing both labeled and unlabeled data, especially using consistency regularization To enhance the model's learning efficiency, we proposed a dualbranch co-training approach. After dividing the training data into two subsets, each branch utilizes a teacherstudent model pair, where the teacher's weights are updated via an exponential moving average (EMA). The framework combines supervised loss and unsupervised loss to optimize the model. Upon label expansion (e.g pseudo labeling), an additional otherside view is introduced, promoting agreement between the branches' predictions on shared data. This loss mitigates errors arising from incorrect pseudo-labels and enhances the overall robustness of the training process. By dynamically adjusting pseudo-label inclusion based on confidence thresholds, our methodology reduces the impact of noisy data and prevents overfitting. As a result, we could demonstrate the effectiveness of the proposed method in leveraging unlabeled data while maintaining high performance.

목차

Abstract
1. Introduction
2. Related Research
2.1 Consistency Regularization
3. Breast Ultrasound Images
4. Otherside View Co-training
5. Experiments and Results
6. Conclusion
Acknowledgement
References

키워드

저자

• Sangmin Lee [ Graduate Student, Major of Data Science, Korea National University of Transportation, Korea ]
• Seokmin Han [ Professor, Major of Data Science, Korea National University of Transportation, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380