Open Access
| Issue |
SHS Web Conf.
Volume 144, 2022
2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
|
|
|---|---|---|
| Article Number | 03008 | |
| Number of page(s) | 4 | |
| Section | Application of Artificial Intelligence Technology and Machine Learning Algorithms | |
| DOI | https://doi.org/10.1051/shsconf/202214403008 | |
| Published online | 26 August 2022 | |
- Zhuang, Fuzhen, et al. “A comprehensive survey on transfer learning.” Proceedings of the IEEE 109.1 (2020): 43-76. [Google Scholar]
- Vinyals, Oriol, et al. “Matching networks for one shot learning.” Advances in neural information processing systems 29 (2016): 3630-3638. [Google Scholar]
- Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. “Siamese neural networks for oneshot image recognition.” ICML deep learning workshop. Vol. 2. 2015. [Google Scholar]
- Snell, Jake, Kevin Swersky, and Richard S. Zemel. “Prototypical networks for few-shot learning.” arXiv preprint arXiv:1703.05175 (2017). [Google Scholar]
- Sung, Flood, et al. “Learning to compare: Relation network for few-shot learning.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [Google Scholar]
- Vinyals, Oriol, et al. “Matching networks for one shot learning.” Advances in neural information processing systems 29 (2016): 3630-3638. [Google Scholar]
- Ren, Mengye, et al. “Meta-learning for semisupervised few-shot classification.” arXiv preprint arXiv:1803.00676 (2018). [Google Scholar]
- Gao, Tianyu, et al. “Hybrid attention-based prototypical networks for noisy few-shot relation classification.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019. [Google Scholar]
- Li, Wenbin, et al. “Distribution consistency based covariance metric networks for few-shot learning.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019. [Google Scholar]
- Gao, Hang, et al. “Low-shot learning via covariancepreserving adversarial augmentation networks.” arXiv preprint arXiv:1810.11730 (2018). [Google Scholar]
- He, Jun, et al. “Memory-augmented relation network for few-shot learning.” Proceedings of the 28th ACM International Conference on Multimedia. 2020. [Google Scholar]
- Garcia, Victor, and Joan Bruna. “Few-shot learning with graph neural networks.” arXiv preprint arXiv:1711.04043 (2017). [Google Scholar]
- Kim, Jongmin, et al. “Edge-labeling graph neural network for few-shot learning.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. [Google Scholar]
- Kang, Dahyun, et al. “Relational Embedding for FewShot Classification.” Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. [Google Scholar]
- Kim, Jongmin, et al. “Edge-labeling graph neural network for few-shot learning.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. [Google Scholar]
- Zhang, Xueting, et al. “RelationNet2: Deep comparison columns for few-shot learning.” arXiv preprint arXiv:1811.07100 (2018). [Google Scholar]
- Zheng, Yan, et al. “Principal characteristic networks for few-shot learning.” Journal of Visual Communication and Image Representation 59 (2019): 563-573. [CrossRef] [Google Scholar]
- Wang, Yaqing, et al. “Generalizing from a few examples: A survey on few-shot learning.” ACM Computing Surveys (CSUR) 53.3 (2020): 1-34. [Google Scholar]
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