Abstract
In recent years, deep learning model has made remarkable achievements in image, voice, text recognition and other fields. However, deep learning model relies heavily on large number of labeled data, which limits its application in the special field of data shortage.
For the practical situation such as lack of data, many scholars carry out research on the few shot learning methods, and there are many typical research directions, among which model-agnostic meta-learning (MAML) is one of them. Aiming at the few shot learning method, this paper systematically expounds the current main research methods on few shot learning, the algorithm of MAML and implements the MAML on the SVHN dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135 (2017). JMLR.org
Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082 (2013)
Hochreiter, S., Younger, A.S., Conwell, P.R.: Learning to learn using gradient descent. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 87–94. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44668-0_13
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, Lille, vol. 2 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)
Normalization, B.: Accelerating deep network training by reducing internal covariate shift. corr.-2015. Vol. abs/1502.03167. http://arxiv.org/abs/1502.03167 (2015)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065 (2016)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, pp. 4077–4087 (2017)
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)
Xia, T.: Research on classification method of small sample image based on meta learning. Master’s thesis, University of Electronic Science and technology (2019)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yang, R., Deng, Y., Zhu, A., Tong, X., Chen, Z. (2021). Few Shot Learning Based on the Street View House Numbers (SVHN) Dataset. In: Jiang, H., Wu, H., Zeng, F. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-030-73429-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-73429-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73428-2
Online ISBN: 978-3-030-73429-9
eBook Packages: Computer ScienceComputer Science (R0)