과제정보
이 논문은 2023년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원 (P0017124, 2023년 산업혁신인재성장지원사업)의 지원을 받아 수행된 연구임.
참고문헌
- Ministry of Science and ICT, National Information Society Agency , "Data Quality Management Guidelines and Construction Guidelines for AI Learning v3.0," Vol.1 Quality Management Guidelines. doi : https://aihub.or.kr/aihubnews/qlityguidance, 2023.
- H. Zhu, J. Shi, J. Wu, "Pick and Learn: Automatic Quality Evaluation for Noisy-labeled Image Segmentation," International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 576-584, 2019. doi: http://dx.doi.org/10.1007/978-3-030-32226-7_64
- Y. Kim, J.M. Kim, Z. Akata, J. Lee, "Large Loss Matters in Weakly Supervised Multi-Label Classification," Computer Vision and Pattern Recognition Conference, pp. 14156-14165, 2022. doi: https://doi.org/10.1109/CVPR52688.2022.01376
- J. Li, R. Socher, and S. C. Hoi, "DivideMix: Learning with noisy labels as semi-supervised learning," International Conference on Learning Representations , pp. 1-14, 2020. doi: https://doi.org/10.1109/IJCNN55064.2022.9892927
- A. Ghosh, N. Manwani, and P. Sastry, "On the robustness of decision tree learning under label noise," The Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 685-697, 2017. doi: https://doi.org/10.1007/978-3-319-57454-7_53
- V. Mnih and G. E. Hinton, "Learning to label aerial images from noisy data," International Conference on Machine Learning, pp. 567-574, 2012.
- M. Bernhardt, D.C. Castro, R. Tanno, A. Schwaighofer, K.C. Tezcan, M. Monteiro, S. Bannur, M.P. Lungren, A. Nori, B. Glocker and J. Alvarez-Valle, "Active label cleaning for improved dataset quality under resource constraints", Nature communications, 13(1), pp.1-11, 2022. doi: https://doi.org/10.1038/s41467-022-28818-3
- O Sener, S Savarese, "Active learning for convolutional neural networks: A core-set approach", International Conference on Learning Representations, 2018. doi: https://doi.org/10.48550/arXiv.1708.00489
- G. Contardo, L. Denoyer and T. Artieres, "A meta-learning approach to one-step active-learning" in arXiv:1706.08334, 2017. doi: https://doi.org/10.48550/arXiv.1706.08334
- P. Bachman, A. Sordoni and A. Trischler, "Learning algorithms for active learning," in arXiv:1708.00088, 2017. doi: https://doi.org/10.48550/arXiv.1708.00088
- A. Byerly and T. Kalganova, "Class Density and Dataset Quality in High-Dimensional, Unstructured Data," arXiv preprint arXiv:2202. 03856, 2022. doi: https://doi.org/10.48550/arXiv.2202.03856
- Y. Zhong, L. Wu, X. Liu and J. Jiang, "Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness," arXiv preprint arXiv:2203.05323, 2022. doi: https://doi.org/10.48550/arXiv.2203.05323
- D. Gamberger, N. Lavrac, and S. Dzeroski, "Noise detection and elimination in data preprocessing: Experiments in medical domains," Applied Artificial Intelligence, vol. 14, no. 2, pp. 205-223, 2000. doi: http://dx.doi.org/10.1080/088395100117124
- T. Liu, D. Tao, "Classification with noisy labels by importance reweighting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 3, pp. 447-461, 2015. doi: http://dx.doi.org/10.1109/TPAMI.2015.2456899
- Y. Sun, Z. Gu, "Using computer vision to recognize construction material: A Trustworthy Dataset Perspective," Resources, Conservation and Recycling, 183, p.106362, 2022. doi: http://dx.doi.org/10.1016/j.resconrec.2022.106362
- H. Song, M. Kim, D. Park, J. Lee, "Learning from noisy labels with deep neural networks: A survey," arXiv preprint arXiv:2007.08199, 2021. doi: http://dx.doi.org/10.1109/TNNLS.2022.3152527
- B. Settles, "Active learning literature survey," Computer Science Technical Report 1648, University of Wisconsin-Madison, January 2010.
- Korea National Information Society Agency, AI Hub, https://aihub. or.kr
- Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo and R. Girshick, "Detectron2", 2019. https://github.com/facebookresearch/detectron2