References
- F.F. Li, R. Fergus, and P. Perona. "One-Shot Learning of Object Categories," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, 2006. DOI: http://dx.doi.org/10.1109/TPAMI.2006.79.
- Y. Cheng, M. Yu, X. Guo and B. Zhou. "Few-Shot Learning with Meta Metric Learners," Proc. 31st Conference on Neural Information Processing Systems (NIPS), 2017.
- A. Nichol, J. Achian and J. Schulman, "On First-Order Meta-Learning Algorithms," arXiv preprint arXiv:1803.02999, 2018.
- S. Ravi and H. Larochelle. "Optimization as a Model for Few-Shot Learning," in Proc. International Conference on Learning Representations, 2017.
- C. Finn, P. Abbeel and S. Levine, "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks," in Proc. 34th International Conference on Machine Learning, Vol. 70, pp. 1126-1135, 2017.
- Z. Li, F. Zhou, F. Chen, and H. Li, "Meta-SGD: Learning to Learn Quickly for Few-Shot Learning," arXiv preprint arXiv:1707.09835, 2017.
- G. Koch, R. Zemel and R. Salakhutdinov, "Siamese Neural Networks for One-Shot Image Recognition," in Proc. International Conference on Learning Representation Deep Learning Workshop, Vol. 2, 2015.
- J. Snell, K. Swersky and R. Zemel, "Prototypical Networks for Few-Shot Learning," In Proc. Advances in Neural Information Processing Systems, pp. 4077-4087, 2017.
- F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales, "Learning to Compare: Relation Network for Few-Shot Learning," in Proc. 2018 IEEE/CVF Conference Vision and Pattern Recognition, pp. 1199-1208, 2018. DOI: http://dx.doi.org/10.1109/CVPR.2018.00131.
- C. Finn, K. Xu, and S. Levine, "Probabilistic Model-Agnostic Meta-Learning," in Proc. Advances in Neural Information Processing Systems, pp. 9516-9527, 2018.
- J. S. Yoon, T. S. Kim, O. Dia, S. W. Kim, Y. Bengio and S. J. Ahn, "Bayesian Model-Agnostic Meta-Learning," in Proc. Advances in Neural Information Processing Systems, pp. 7332-7342, 2018.
- A. Antoniou, H. Edwards, and A. Storkey, "How to Train Your MAML," arXiv preprint arXiv:1810.09502, 2019.
- R. Vuorio, S. -H. Sun, H. Hu and J. J. Lim, "Toward Multimodal Model-Agnostic Meta-Learning," arXiv preprint arXiv:1812.07172, 2018.
- K. Li and D.-K. Kang, "FAST-ADAM in Semi-Supervised Generative Adversarial Networks," International Journal of Internet, Broadcasting and Communication (IJIBC), Vol. 11, No. 4, pp. 31-36, Nov. 2019. DOI: http://dx.doi.org/10.7236/IJIBC.2019.11.4.31.
- Z.-Y. Wang and D.-K. Kang, "Experimental Analysis of Equilibrization in Binary Classification for Non-Image Imbalanced Data Using Wasserstein GAN," International Journal of Internet, Broadcasting and Communication (IJIBC), Vol. 11, No. 4, pp. 37-42, Nov. 2019. DOI: http://dx.doi.org/10.7236/IJIBC.2019.11.4.37.