Meta-Learning Algorithms for Rapid Model Adaptation

  • Authors

    • Dr. Fernanda Lopes Department of IoT & Data Analytics, Universidade Estadual de Campinas, Brazil. Author

    Published 2026-01-08

  • Meta-Learning, Few-Shot Learning, Rapid Adaptation, Model Generalization, Optimization-Based Learning, Transfer Learning, Task Distribution

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    F. Lopes, “Meta-Learning Algorithms for Rapid Model Adaptation”, IJMLPA, vol. 1, no. 1, pp. 54–63, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijmlpa/article/view/64

  • Abstract

    The concept of learning to learn has been dubbed as meta-learning and has proven to be an effective paradigm that allows machine learning models to learn new tasks very quickly with as little data as possible. Classical deep learning methods often need large labeled datasets and need retraining to operate again in a new setting or task, which restricts their use in dynamic and data-sparse conditions or real-time methods. Meta-learning algorithms solve this issue by learning transferable knowledge over a distribution of tasks and so enabling models to generalize with high efficiency to unknown tasks during rapid learning processes. The present paper researches in-depth the meta-learning algorithm of rapid model adaptation focusing on theoretical background, algorithm frameworks, and implementation. Our literature survey presents the various optimization based, metric based and model based methods of meta-learning with their strengths and limitations. It is suggested to formulate the meta-training and meta-testing process in a unified methodology that encompasses both the task-level optimization and the parameter initialisation strategies as well as adaptation dynamics. Whereas experimental analysis at few-shot classification benchmarks has shown the success of meta-learning to provide increased convergence speed, data-efficiency, and generalization. The findings prove that meta-learning algorithms are much better performing than the traditional transfer learning methods under low-data conditions. Lastly, the main challenges, unresolved issues in research, and future directions are addressed, such as the aspect of scalability, stability, and the implementation of the system in practice.

  • References

    [1] Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML).

    [2] Nichol, A., Achiam, J., & Schulman, J. (2018). On First-Order Meta-Learning Algorithms. arXiv preprint arXiv:1803.02999.

    [3] Li, Z., Zhou, F., Chen, F., & Li, H. (2017). Meta-SGD: Learning to Learn Quickly for Few-Shot Learning. arXiv preprint arXiv:1707.09835.

    [4] Ravi, S., & Larochelle, H. (2017). Optimization as a Model for Few-Shot Learning. International Conference on Learning Representations (ICLR).

    [5] Snell, J., Swersky, K., & Zemel, R. (2017). Prototypical Networks for Few-Shot Learning. Advances in Neural Information Processing Systems (NeurIPS).

    [6] Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS).

    [7] Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., & Hospedales, T. (2018). Learning to Compare: Relation Network for Few-Shot Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

    [8] Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese Neural Networks for One-shot Image Recognition. ICML Deep Learning Workshop.

    [9] Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016). Meta-Learning with Memory-Augmented Neural Networks. Proceedings of the 33rd International Conference on Machine Learning (ICML).

    [10] Santoro, A., et al. (2018). Measuring Abstract Reasoning in Neural Networks. Proceedings of the 35th International Conference on Machine Learning (ICML).

    [11] Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2018). A Simple Neural Attentive Meta-Learner. International Conference on Learning Representations (ICLR).

    [12] Wang, J. X., et al. (2016). Learning to Reinforcement Learn. arXiv preprint arXiv:1611.05763.

    [13] Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. (2018). Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. International Conference on Learning Representations (ICLR).

    [14] Hospedales, T., Antoniou, A., Micaelli, P., & Storkey, A. (2021). Meta-Learning in Neural Networks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

    [15] Vanschoren, J. (2019). Meta-Learning: A Survey. arXiv preprint arXiv:1810.03548.

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