Acknowledgement
이 연구는 2022년 정부(방위사업청)의 재원으로 국방과학연구소의 지원을 받아 수행된 미래도전국방기술연구개발사업임(No. 915029201).
References
- F, Pierre, et al., "Sharpness-aware minimization for efficiently improving generalization," arXiv preprint arXiv:2010.01412, 2020.
- M, Wortsman, et al., "Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time," International Conference on Machine Learning, PMLR, 2022.
- H, M. Kabir, "Reduction of Class Activation Uncertainty with Background Information," arXiv preprint arXiv:2305.03238, 2023.
- Z, Yang, et al., "SAR image classification method based on improved capsule network," Journal of Physics: Conference Series, Vol. 1693, No. 1, IOP Publishing, 2020.
- S, Chen, et al., "Target classification using the deep convolutional networks for SAR images," IEEE transactions on geoscience and remote sensing 54.8, 4806-4817, 2016.
- H, Ren, et al., "Extended convolutional capsule network with application on SAR automatic target recognition," Signal Processing 183 : 108021, 2021.
- Q, Xie, et al., "Self-training with noisy student improves imagenet classification," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020.
- E, Arazo, et al., "Pseudo-labeling and confirmation bias in deep semi-supervised learning," 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020.
- D, Berthelot, et al., "Mixmatch: A holistic approach to semi-supervised learning," Advances in neural information processing systems 32, 2019.
- D, Berthelot, et al., "Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring," arXiv preprint arXiv:1911.09785, 2019.
- K, Sohn, Kihyuk, et al., "Fixmatch: Simplifying semi-supervised learning with consistency and confidence," Advances in neural information processing systems 33 : 596-608, 2020.
- T, Chen, et al., "A simple framework for contrastive learning of visual representations," International conference on machine learning, PMLR, 2020.
- R, Shams, "Semi-supervised classification for natural language processing," arXiv preprint arXiv:1409.7612, 2014.
- A, Anaby-Tavor, et al., "Do not have enough data? Deep learning to the rescue!," Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 05, 2020.
- B, Zhang, et al., "Censer: Curriculum semi-supervised learning for speech recognition based on self-supervised pre-training," arXiv preprint arXiv:2206.08189, 2022.
- V, Tsouvalas, et al., "Federated self-training for semi-supervised audio recognition," ACM Transactions on Embedded Computing Systems 21.6 : 1-26, 2022.
- M, Sajjadi, et al., "Regularization with stochastic transformations and perturbations for deep semi-supervised learning," Advances in neural information processing systems 29, 2016.
- X, Wang, et al., "Contrastive learning with stronger augmentations," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
- E, Cubuk, et al., "Randaugment: Practical automated data augmentation with a reduced search space," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020.
- ED, Cubuk, et al., "Autoaugment: Learning augmentation strategies from data," Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019.
- Y, Lei, et al., "Synthetic Images Augmentation for Robust SAR Target Recognition," 2021 The 5th International Conference on Video and Image Processing, 2021.
- C, Hyunho, and J, Jechang, et al., "Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform," Remote Sensing 11.10 (2019): 1184.
- X, Zhang, et al., "A Novel Data Augmentation Method for SAR Image Target Detection and Recognition," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 2021.
- M, Zhang, et al., "Data augmentation method of SAR image dataset," IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2018.
- K, Choi, et al., "Deep Cascade Network for Noise-Robust SAR Ship Detection With Label Augmentation," in IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp. 1-5, 2022, Art No. 4514005, doi: 10.1109/LGRS.2022.3205715.
- ER, Keydel, et al., "MSTAR extended operating conditions: A tutorial," Algorithms for Synthetic Aperture Radar Imagery III 2757, 228-242, 1996.
- S, Zagoruykond Nikos Komodakis, "Wide residual networks," arXiv preprint arXiv:1605.07146, 2016.
- C, Coman, "A deep learning SAR target classification experiment on MSTAR dataset," 2018 19th international radar symposium(IRS), IEEE, 2018.