[19] L. Liu, H. Jiang, P. He, W. Chen, X. Liu, J. Gao, and J. Han. On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265, 2019.
[20] Y. Liu, B. Schiele, and Q. Sun. An ensemble of epoch-wise empirical bayes for few-shot learning. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVI 16, pages 404-421. Springer, 2020.
[21] N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141, 2017.
[22] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of machine learning. MIT press, 2018.
[23] T. Munkhdalai and H. Yu. Meta networks. In International conference on machine learning, pages 2554-2563. PMLR, 2017.
[24] A. Nichol, J. Achiam, and J. Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[25] E. Park and J. B. Oliva. Meta-curvature. Advances in Neural Information Processing Systems, 32, 2019.
[26] A. Phaphuangwittayakul, Y. Guo, and F. Ying. Fast adaptive meta-learning for few-shot image generation. IEEE Transactions on Multimedia, 24:2205-2217, 2021.
[27] B. T. Polyak. Some methods of speeding up the convergence of iteration methods. Ussr computational mathematics and mathematical physics, 4(5):1-17, 1964.
[28] J. Rajendran, A. Irpan, and E. Jang. Meta-learning requires meta-augmentation. Advances in Neural Information Processing Systems, 33:5705-5715, 2020.
[29] A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine. Meta-learning with implicit gradients. Advances in neural information processing systems, 32, 2019.
[30] S. Ravi and H. Larochelle. Optimization as a model for few-shot learning. In International conference on learning representations, 2017.
[31] M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel. Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676, 2018.
[32] S. Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
[33] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015.
[34] A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. Meta-learning with latent embedding optimization. arXiv preprint arXiv:1807.05960, 2018.
[35] A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. Meta-learning with memory-augmented neural networks. In International conference on machine learning, pages 1842-1850. PMLR, 2016.
[36] J. Snell, K. Swersky, and R. Zemel. Prototypical networks for few-shot learning. Advances in neural information processing systems, 30, 2017.
[37] Y. Song, T. Wang, S. K. Mondal, and J. P. Sahoo. A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. arXiv preprint arXiv:2205.06743, 2022.
[38] Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele. Meta-transfer learning for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 403-412, 2019.