# Small Data Papers
###### tags: `Small Data`
## Summary Papers:
* Meta Learning
* [Hospedales, T., et al. (2020). Meta-learning in neural networks: A survey. arXiv preprint arXiv:2004.05439.](https://arxiv.org/abs/2004.05439)(Cited by 81)
* [Huisman, M., van Rijn, J. N., & Plaat, A. (2020). A Survey of Deep Meta-Learning. arXiv preprint arXiv:2010.03522.](https://arxiv.org/abs/2010.03522)(Cited by 1)
* [Vanschoren, J. (2018). Meta-learning: A survey. arXiv preprint arXiv:1810.03548.](https://arxiv.org/abs/1810.03548)(Cited by 159)
* Few-Shot Learning
* [Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (CSUR), 53(3), 1-34.](https://dl.acm.org/doi/abs/10.1145/3386252)(Cited by 141)
* [Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019). A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-37.](https://dl.acm.org/doi/abs/10.1145/3293318)(Cited by 97)
* Meta Dataset: [Triantafillou., et al. (2019). Meta-dataset: A dataset of datasets for learning to learn from few examples. arXiv preprint arXiv:1903.03096.](https://arxiv.org/abs/1903.03096)(Cited by 124)

## Meta Learning
* MAML( Cited by 2897): [Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. arXiv preprint arXiv:1703.03400.](https://arxiv.org/abs/1703.03400)
* Reptile(Cited by 490): [Nichol, A., Achiam, J., & Schulman, J. (2018). On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999.](https://arxiv.org/abs/1803.02999)
* Meta-SGD(Cited by 360): [Li, Z., Zhou, F., Chen, F., & Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835.](https://arxiv.org/abs/1707.09835)
* LSTM Meta-Learner(Cited by 1216): [Ravi, S., & Larochelle, H. (2016). Optimization as a model for few-shot learning.](https://openreview.net/forum?id=rJY0-Kcll&noteId=ryq49XyLg)
* LSTM optimizer( Cited by 1039): [Marcin Andrychowicz., et al. (2016). Learning to learn by gradient descent by gradient descent.](https://arxiv.org/abs/1606.04474)
* ANIL(Cited by 520): [Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2017). A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141.](https://arxiv.org/abs/1707.03141)

## Few Shot Learning
* Siamese Network( Cited by 1481): [Bromley, J., et al. (1993). Signature verification using a “siamese” time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence, 7(04), 669-688.](https://www.worldscientific.com/doi/abs/10.1142/S0218001493000339)
* Triplet loss(from Facenet,Cited by 7406)[Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).
](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html)
* Quadraplet loss(Cited by 714)[Chen, W., Chen, X., Zhang, J., & Huang, K. (2017). Beyond triplet loss: a deep quadruplet network for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 403-412).
](https://arxiv.org/abs/1704.01719v1)
* Prototypical Network(Cited by 1961): [Snell, J., Swersky, K., & Zemel, R. S. (2017). Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175.](https://arxiv.org/abs/1703.05175)
* Matching Network( Cited by 2301): [Vinyals, O., et al. (2016). Matching networks for one shot learning. Advances in neural information processing systems, 29, 3630-3638.](https://papers.nips.cc/paper/2016/hash/90e1357833654983612fb05e3ec9148c-Abstract.html)
* Relation Network(Cited by 1031): [Sung, F., et al. (2018). Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1199-1208).](https://openaccess.thecvf.com/content_cvpr_2018/html/Sung_Learning_to_Compare_CVPR_2018_paper.html)
* NTM(Cited by 1723): [Graves, A., Wayne, G., & Danihelka, I. (2014). Neural turing machines. arXiv preprint arXiv:1410.5401.](https://arxiv.org/abs/1410.5401)
* MANN(Cited by 343): [Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016). One-shot learning with memory-augmented neural networks. arXiv preprint arXiv:1605.06065.](https://arxiv.org/abs/1605.06065)
* MANN(Cited by 968)[Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016, June). Meta-learning with memory-augmented neural networks. In International conference on machine learning (pp. 1842-1850). PMLR.](http://proceedings.mlr.press/v48/santoro16.html)
* Bayesian Program Learning(Cited by 1698): [Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338.](https://science.sciencemag.org/content/350/6266/1332/)

## Generative Adversarial Network(GAN)
* Data augmentation generative adversarial networks(Cited by 414): [Antreas Antoniou, Amos Storkey, and Harrison Edwards (2017)](https://arxiv.org/abs/1711.04340)
* (Nvidia) GANimal, FUNIT(Cited by 159): [Ming-Yu Liu., et al.(2019). Few-Shot Unsupervised Image-to-Image Translation](https://arxiv.org/abs/1905.01723)
* (Nvidia) StyleGAN2-ADA(Cited by 24): [Tero Karras., et al.(2020). Training Generative Adversarial Networks with Limited Data](https://arxiv.org/abs/2006.06676)
* IAGAN: [Motamed, Saman, and Farzad Khalvati.(2020). Inception Augmentation Generative Adversarial Network](https://arxiv.org/abs/2006.03622)(GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images)
* Delta-encoder(Cited by 101): [Eli Schwartz., et al. (2018). Delta-encoder: an effective sample synthesis method for few-shot object recognition](https://arxiv.org/abs/1806.04734)
* MetaGAN(Cited by 130): [Ruixiang Zhang., et al.(2018). MetaGAN: An Adversarial Approach to Few-Shot Learning](https://proceedings.neurips.cc/paper/2018/file/4e4e53aa080247bc31d0eb4e7aeb07a0-Paper.pdf)
* FSL Talking Head(Cited by 161): [Egor Zakharov., et al.(2019). Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://arxiv.org/abs/1905.08233)
* Adversarial Meta-Learning(Cited by 13): [Chengxiang Yin., et al. (2018). Adversarial Meta-Learning](https://arxiv.org/abs/1806.03316)
* F2GAN(Cited by 3): [Yan Hong., et al.(2020). F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation](https://arxiv.org/abs/2008.01999)
* Few-shot Classifier GAN(Cited by 6): [Adamu Ali-Gombe., et al.(2018). Few-shot Classifier GAN](http://www.animlife.com/publications/ijcnn18.pdf)
* CP-AAN(Cited by 43): [Gao, H., et al. (2018). Low-shot learning via covariance-preserving adversarial augmentation networks. In Advances in Neural Information Processing Systems (pp. 975-985).](https://papers.nips.cc/paper/2018/hash/81448138f5f163ccdba4acc69819f280-Abstract.html)
* AnoGAN(Cited by 792): [Thomas Schlegl., et al.(2017). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://arxiv.org/abs/1703.05921)
* GANomaly(Cited by 227): [Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon. (2018). Semi-Supervised Anomaly Detection via Adversarial Training](https://arxiv.org/abs/1805.06725)

## Github of Books
* [Hands-On Meta Learning With Python](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python)
* [Hands-On One-Shot Learning With Python](https://github.com/shruti-jadon/Hands-on-One-Shot-Learning)