# Zero/Few Shot Learning and Meta Learning

## Resources
- GitHub Link: [[sudharsan13296/Awesome-Meta-Learning]](https://github.com/sudharsan13296/Awesome-Meta-Learning).
- [2019 ICML Tutorial] **Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning**, [[homepage]](https://sites.google.com/view/icml19metalearning), [[slides]](https://icml.cc/media/Slides/icml/2019/halla%2810-09-15%29-10-13-00-4340-meta-learning_.pdf).

## Zero/Few Shot Learning
- [2018 CVPR] **Zero-Shot Visual Recognition using Semantics-Preserving**, [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Zero-Shot_Visual_Recognition_CVPR_2018_paper.pdf), [[bibtex]](/Bibtex/Zero-Shot%20Visual%20Recognition%20using%20Semantics-Preserving.bib), sources: [[zjuchenlong/sp-aen.cvpr18]](https://github.com/zjuchenlong/sp-aen.cvpr18).
- [2019 ArXiv] **Few-Shot Text Classification with Induction Network**, [[paper]](https://arxiv.org/pdf/1902.10482.pdf), [[bibtex]](/Bibtex/Few-Shot%20Text%20Classification%20with%20Induction%20Network.bib).
- [2019 CVPR] **Meta-Transfer Learning for Few-Shot Learning**, [[paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Meta-Transfer_Learning_for_Few-Shot_Learning_CVPR_2019_paper.pdf), [[bibtex]](/Bibtex/Meta-Transfer%20Learning%20for%20Few-Shot%20Learning.bib), sources: [[yaoyao-liu/meta-transfer-learning]](https://github.com/yaoyao-liu/meta-transfer-learning).
- [2019 NeurIPS] **Learning to Self-Train for Semi-Supervised Few-Shot Classification**, [[paper]](https://papers.nips.cc/paper/2019/file/bf25356fd2a6e038f1a3a59c26687e80-Paper.pdf), [[bibtex]](/Bibtex/Learning%20to%20Self-Train%20for%20Semi-Supervised%20Few-Shot%20Classification.bib), sources: [[xinzheli1217/learning-to-self-train]](https://github.com/xinzheli1217/learning-to-self-train).
- [2020 AAAI] **Meta-Learning for Generalized Zero-Shot Learning**, [[paper]](/Documents/Papers/Meta-Learning%20for%20Generalized%20Zero-Shot%20Learning.pdf), [[bibtex]](/Bibtex/Meta-Learning%20for%20Generalized%20Zero-Shot%20Learning.bib).
- [2020 NeurIPS] **Interventional Few-Shot Learning**, [[paper]](https://proceedings.neurips.cc//paper/2020/file/1cc8a8ea51cd0adddf5dab504a285915-Paper.pdf), [[bibtex]](/Bibtex/Interventional%20Few-Shot%20Learning.bib), sources: [[yue-zhongqi/ifsl]](https://github.com/yue-zhongqi/ifsl).
- [2021 ICLR] **Self-Training for Few-Shot Transfer Across Extreme Task Differences**, [[paper]](https://openreview.net/pdf?id=O3Y56aqpChA), [[bibtex]](/Bibtex/Self-Training%20for%20Few-Shot%20Transfer%20Across%20Extreme%20Task%20Differences.bib).

## Meta Learning
- [2016 ICML] **Meta-Learning with Memory-Augmented Neural Networks**, [[paper]](http://proceedings.mlr.press/v48/santoro16.pdf), [[bibtex]](/Bibtex/Meta-Learning%20with%20Memory-Augmented%20Neural%20Networks.bib), [[supplementary]](http://proceedings.mlr.press/v48/santoro16-supp.pdf), sources: [[snowkylin/ntm]](https://github.com/snowkylin/ntm), [[ywatanabex/ntm-meta-learning]](https://github.com/ywatanabex/ntm-meta-learning).
- [2017 ICML] **Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**, [[paper]](https://arxiv.org/pdf/1703.03400.pdf), [[bibtex]](/Bibtex/Model-Agnostic%20Meta-Learning%20for%20Fast%20Adaptation%20of%20Deep%20Networks.bib), [[blog]](https://zhuanlan.zhihu.com/p/57864886), sources: [[cbfinn/maml]](https://github.com/cbfinn/maml), [[cbfinn/maml_rl]](https://github.com/cbfinn/maml_rl), [[dragen1860/MAML-Pytorch]](https://github.com/dragen1860/MAML-Pytorch), [[tristandeleu/pytorch-maml-rl]](https://github.com/tristandeleu/pytorch-maml-rl), [[katerakelly/pytorch-maml]](https://github.com/katerakelly/pytorch-maml), [[learnables/learn2learn]](https://github.com/learnables/learn2learn).
- [2018 ArXiv] **On First-Order Meta-Learning Algorithms**, [[paper]](https://arxiv.org/pdf/1803.02999.pdf), [[bibtex]](/Bibtex/On%20First-Order%20Meta-Learning%20Algorithms.bib), sources: [[openai/supervised-reptile]](https://github.com/openai/supervised-reptile), [[greentfrapp/maml-reptile]](https://github.com/greentfrapp/maml-reptile).
- [2018 ICLR] **A Simple Neural Attentive Meta-Learner**, [[paper]](https://openreview.net/pdf?id=B1DmUzWAW), [[bibtex]](/Bibtex/A%20Simple%20Neural%20Attentive%20Meta-Learner.bib), sources: [[eambutu/snail-pytorch]](https://github.com/eambutu/snail-pytorch), [[sagelywizard/snail]](https://github.com/sagelywizard/snail).
- [2019 EMNLP] **Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks**, [[paper]](https://www.aclweb.org/anthology/D19-1112.pdf), [[bibtex]](/Bibtex/Investigating%20Meta-Learning%20Algorithms%20for%20Low-Resource%20Natural%20Language%20Understanding%20Tasks.bib), [[supplementary]](https://www.aclweb.org/anthology/attachments/D19-1112.Attachment.pdf).
- [2020 ICLR] **Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks**, [[paper]](https://openreview.net/pdf?id=rkeZIJBYvr), [[bibtex]](/Bibtex/Learning%20to%20Balance%20-%20Bayesian%20Meta-Learning%20for%20Imbalanced%20and%20Out-of-distribution%20Tasks.bib), sources: [[haebeom-lee/l2b]](https://github.com/haebeom-lee/l2b).