小樣本學習論文總結（few-shot learning）

2011

1. Lake, Brenden, et al. "One shot learning of simple visual concepts." Proceedings of the annual meeting of the cognitive science society. Vol. 33. No. 33. 2011. [paper]

2013

1. Socher, Richard, et al. "Zero-shot learning through cross-modal transfer." Advances in neural information processing systems. 2013.[paper]
2. Norouzi, Mohammad, et al. "Zero-shot learning by convex combination of semantic embeddings." arXiv preprint arXiv:1312.5650 (2013).[paper]
3. Lake, Brenden M., Ruslan R. Salakhutdinov, and Josh Tenenbaum. "One-shot learning by inverting a compositional causal process." Advances in neural information processing systems. 2013.[paper]

2015

1. Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. "Siamese neural networks for one-shot image recognition." ICML Deep Learning Workshop. Vol. 2. 2015. [paper]

2016

1. Ravi, Sachin, and Hugo Larochelle. "Optimization as a model for few-shot learning." (2016). [paper]
2. Santoro, Adam, et al. "Meta-learning with memory-augmented neural networks." International conference on machine learning. 2016.[paper]
3. Xie, Ruobing, et al. "Representation learning of knowledge graphs with entity descriptions." Thirtieth AAAI Conference on Artificial Intelligence. 2016. [paper]
4. Ma, Yukun, Erik Cambria, and Sa Gao. "Label embedding for zero-shot fine-grained named entity typing." Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2016.[paper]
5. Vinyals, Oriol, et al. "Matching networks for one shot learning." Advances in neural information processing systems. 2016.[paper]
6. Santoro, Adam, et al. "One-shot learning with memory-augmented neural networks." arXiv preprint arXiv:1605.06065(2016).[paper]

2017

1. Garcia, Victor, and Joan Bruna. "Few-shot learning with graph neural networks." arXiv preprint arXiv:1711.04043 (2017).[paper]
2. Snell, Jake, Kevin Swersky, and Richard Zemel. "Prototypical networks for few-shot learning." Advances in Neural Information Processing Systems. 2017. [paper]
3. Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-agnostic meta-learning for fast adaptation of deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.[paper]
4. Munkhdalai, Tsendsuren, and Hong Yu. "Meta networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.[paper]
5. Mishra, Nikhil, et al. "A simple neural attentive meta-learner." arXiv preprint arXiv:1707.03141 (2017).[paper]
6. Triantafillou, Eleni, Richard Zemel, and Raquel Urtasun. "Few-shot learning through an information retrieval lens." Advances in Neural Information Processing Systems. 2017.[paper]

2018

1. Yu, Mo, et al. "Diverse few-shot text classification with multiple metrics." arXiv preprint arXiv:1805.07513 (2018).[paper]
2. Sung, Flood, et al. "Learning to compare: Relation network for few-shot learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.[paper]
3. Han, Xu, et al. "Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation." arXiv preprint arXiv:1810.10147 (2018).[paper]
4. Ren, Mengye, et al. "Meta-learning for semi-supervised few-shot classification." arXiv preprint arXiv:1803.00676 (2018).[paper]
5. Gidaris, Spyros, and Nikos Komodakis. "Dynamic few-shot visual learning without forgetting." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.[paper]

2019

1. Gao, Tianyu, et al. "Hybrid attention-based prototypical networks for noisy few-shot relation classification." Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence,(AAAI-19), New York, USA. 2019.[paper]
2. Geng, Ruiying, et al. "Few-Shot Text Classification with Induction Network." arXiv preprint arXiv:1902.10482 (2019).[paper]