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https://github.com/sudharsan13296/Awesome-Meta-Learning

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
https://github.com/sudharsan13296/Awesome-Meta-Learning

List: Awesome-Meta-Learning

deep-meta-learning few-shot-learning meta-reinforcement metalearning one-shot-learning zero-shot-learning

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A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

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README 

        
# Awesome Meta Learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources. 



# [Table of Contents]()



* [Papers and Code](#Papers-and-Code)

* [Books](#Books)

* [Libraries](#Libraries)

* [Blogs](#Blogs)

* [Lecture Videos](#)

* [Datasets](#Datasets)

* [Workshops](#Workshops)

* [Researchers](#Researchers)



## Check out my Deep Reinforcement Learning Repo [here.](https://github.com/sudharsan13296/Deep-Reinforcement-Learning-With-Python)



## [Papers and Code]()



A curated set of papers along with code.



### [Zero-Shot / One-Shot / Few-Shot / Low-Shot Learning]()



* __Siamese Neural Networks for One-shot Image Recognition__, (2015), _Gregory Koch, Richard Zemel, Ruslan Salakhutdinov_. [[pdf]](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/02.%20Face%20and%20Audio%20Recognition%20using%20Siamese%20Networks/2.4%20Face%20Recognition%20Using%20Siamese%20Network.ipynb) 



* __Prototypical Networks for Few-shot Learning__, (2017), _Jake Snell, Kevin Swersky, Richard S. Zemel_. [[pdf]](https://arxiv.org/pdf/1703.05175.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/03.%20Prototypical%20Networks%20and%20its%20Variants/3.3%20Omniglot%20Character%20set%20classification%20using%20Prototypical%20Network.ipynb)



* __Gaussian Prototypical Networks for Few-Shot Learning on Omniglot__ (2017), _Stanislav Fort_. [[pdf]](https://arxiv.org/pdf/1708.02735.pdf) [[code]](https://github.com/stanislavfort/gaussian-prototypical-networks) 



* __Matching Networks for One Shot Learning__, (2017), _Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra_. [[pdf]](https://arxiv.org/pdf/1606.04080.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/04.%20Relation%20and%20Matching%20Networks%20Using%20Tensorflow/4.9%20Matching%20Networks%20Using%20Tensorflow.ipynb) 



* __Learning to Compare: Relation Network for Few-Shot Learning__, (2017), _Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales_. [[pdf]](https://arxiv.org/pdf/1711.06025.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/04.%20Relation%20and%20Matching%20Networks%20Using%20Tensorflow/4.5%20Building%20Relation%20Network%20Using%20Tensorflow.ipynb)



* __One-shot Learning with Memory-Augmented Neural Networks__, (2016), _Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap_. [[pdf]](https://arxiv.org/pdf/1605.06065.pdf) [[code]](https://github.com/vineetjain96/one-shot-mann)



* __Optimization as a Model for Few-Shot Learning__, (2016), _Sachin Ravi and Hugo Larochelle_. [[pdf]](https://openreview.net/pdf?id=rJY0-Kcll) [[code]](https://github.com/gitabcworld/FewShotLearning)



* __An embarrassingly simple approach to zero-shot learning__, (2015), _B Romera-Paredes, Philip H. S. Torr_. [[pdf]](http://proceedings.mlr.press/v37/romera-paredes15.pdf) [[code]](https://github.com/bernard24/Embarrassingly-simple-ZSL)



* __Low-shot Learning by Shrinking and Hallucinating Features__, (2017), _Bharath Hariharan, Ross Girshick_.  [[pdf]](https://arxiv.org/pdf/1606.02819.pdf) [[code]](https://github.com/facebookresearch/low-shot-shrink-hallucinate)



* __Low-shot learning with large-scale diffusion__, (2018), _Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou_. 

[[pdf]](https://arxiv.org/pdf/1706.02332v2.pdf) [[code]](https://github.com/facebookresearch/low-shot-with-diffusion)



* __Low-Shot Learning with Imprinted Weights__, (2018), _Hang Qi, Matthew Brown, David G. Lowe_. [[pdf]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Low-Shot_Learning_With_CVPR_2018_paper.pdf) [[code]](https://github.com/YU1ut/imprinted-weights) 



* __One-Shot Video Object Segmentation__, (2017), _S. Caelles and K.K. Maninis and J. Pont-Tuset and L. Leal-Taixe' and D. Cremers and L. Van Gool_. [[pdf]](http://vision.ee.ethz.ch/~cvlsegmentation/osvos/) [[code]](https://github.com/scaelles/OSVOS-TensorFlow)



* __One-Shot Learning for Semantic Segmentation__, (2017), _Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots_. [[pdf]](https://arxiv.org/abs/1709.03410) [[code]](https://github.com/lzzcd001/OSLSM)



* __Few-Shot Segmentation Propagation with Guided Networks__, (2018), _Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine_. [[pdf]](https://arxiv.org/abs/1806.07373) [[code]](https://github.com/shelhamer/revolver)



* __Few-Shot Semantic Segmentation with Prototype Learning__, (2018), _Nanqing Dong and Eric P. Xing_. [[pdf]](http://bmvc2018.org/contents/papers/0255.pdf)



* __Dynamic Few-Shot Visual Learning without Forgetting__, (2018), _Spyros Gidaris, Nikos Komodakis_. [[pdf]](https://arxiv.org/pdf/1804.09458.pdf) [[code]](https://github.com/gidariss/FewShotWithoutForgetting)



* __Feature Generating Networks for Zero-Shot Learning__, (2017), _Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata_. [[pdf]](https://arxiv.org/pdf/1712.00981.pdf)



* __Meta-Learning Deep Visual Words for Fast Video Object Segmentation__, (2019), _Harkirat Singh Behl, Mohammad Najafi, Anurag Arnab, Philip H.S. Torr_. [[pdf]](https://arxiv.org/pdf/1812.01397.pdf)



## [Model Agnostic Meta Learning]()



* __Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks__, (2017), _Chelsea Finn, Pieter Abbeel, Sergey Levine_. [[pdf]](https://arxiv.org/pdf/1703.03400.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/06.%20MAML%20and%20it's%20Variants/6.5%20Building%20MAML%20From%20Scratch.ipynb)



* __Adversarial Meta-Learning__, (2018), _Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang_. [[pdf]](https://arxiv.org/pdf/1806.03316.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/06.%20MAML%20and%20it's%20Variants/6.7%20Building%20ADML%20From%20Scratch.ipynb)



* __On First-Order Meta-Learning Algorithms__, (2018), _Alex Nichol, Joshua Achiam, John Schulman_. [[pdf]](https://arxiv.org/pdf/1803.02999.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/07.%20Meta-SGD%20and%20Reptile%20Algorithms/7.7%20Sine%20wave%20Regression%20Using%20Reptile.ipynb)



* __Meta-SGD: Learning to Learn Quickly for Few-Shot Learning__, (2017), _Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li_. [[pdf]](https://arxiv.org/pdf/1707.09835.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/07.%20Meta-SGD%20and%20Reptile%20Algorithms/7.4%20Building%20Meta-SGD%20from%20Scratch.ipynb) 



* __Gradient Agreement as an Optimization Objective for Meta-Learning__, (2018), _Amir Erfan Eshratifar, David Eigen, Massoud Pedram_. [[pdf]](https://arxiv.org/pdf/1810.08178.pdf) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/08.%20Gradient%20Agreement%20As%20An%20Optimization%20Objective/8.4%20Building%20Gradient%20Agreement%20Algorithm%20with%20MAML.ipynb)



* __Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace__, (2018), _Yoonho Lee, Seungjin Choi_. [[pdf]](https://arxiv.org/pdf/1801.05558.pdf) [[code]](https://github.com/yoonholee/MT-net)



* __A Simple Neural Attentive Meta-Learner__, (2018), _Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel_. [[pdf]](https://arxiv.org/pdf/1707.03141.pdf) [[code]](https://github.com/eambutu/snail-pytorch)



* __Personalizing Dialogue Agents via Meta-Learning__, (2019), _Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung_. [[pdf]](https://arxiv.org/pdf/1905.10033.pdf) [[code]](https://github.com/HLTCHKUST/PAML) 



* __How to train your MAML__, (2019), _Antreas Antoniou, Harrison Edwards, Amos Storkey_. [[pdf]](https://arxiv.org/pdf/1810.09502.pdf) [[code]](https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch)



* __Learning to learn by gradient descent by gradient descent__, (206), _Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas_. [[pdf]](https://arxiv.org/pdf/1606.04474.pdf) [[code]](https://github.com/deepmind/learning-to-learn)



* __Unsupervised Learning via Meta-Learning__, (2019), _Kyle Hsu, Sergey Levine, Chelsea Finn_. [[pdf]](https://arxiv.org/pdf/1810.02334.pdf) [[code]](https://github.com/hsukyle/cactus-maml)



* __Few-Shot Image Recognition by Predicting Parameters from Activations__, (2018), _Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille_. [[pdf]](https://arxiv.org/pdf/1706.03466.pdf) [[code]](https://github.com/joe-siyuan-qiao/FewShot-CVPR)



* __One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning__, (2018), _Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Pieter Abbeel, Sergey Levine_, [[pdf]](https://arxiv.org/pdf/1802.01557.pdf) [[code]](https://github.com/aravind0706/upn)



* __MetaGAN: An Adversarial Approach to Few-Shot Learning__, (2018), _ZHANG, Ruixiang and Che, Tong and Ghahramani, Zoubin and Bengio, Yoshua and Song, Yangqiu_. [[pdf]](http://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning.pdf) 



* __Fast Parameter Adaptation for Few-shot Image Captioning and Visual Question Answering__,(2018), _Xuanyi Dong, Linchao Zhu, De Zhang, Yi Yang, Fei Wu_. [[pdf]](https://xuanyidong.com/pdf/FPAIT-MM-18.pdf) 



* __CAML: Fast Context Adaptation via Meta-Learning__, (2019), _Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson_. [[pdf]](https://arxiv.org/pdf/1810.03642.pdf) 



* __Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems__, (2019), _Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings_. [[pdf]](https://arxiv.org/pdf/1905.05644.pdf)



* __MIND: Model Independent Neural Decoder__, (2019), _Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan_. [[pdf]](https://arxiv.org/pdf/1903.02268.pdf)



* __Toward Multimodal Model-Agnostic Meta-Learning__, (2018), _Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim_. [[pdf]](https://arxiv.org/pdf/1812.07172.pdf) 



* __Alpha MAML: Adaptive Model-Agnostic Meta-Learning__, (2019), _Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr._  [[pdf]](https://arxiv.org/pdf/1905.07435.pdf)



* __Online Meta-Learning__, (2019), Chelsea Finn, _Aravind Rajeswaran, Sham Kakade, Sergey Levine_. [[pdf]](https://arxiv.org/pdf/1902.08438.pdf)



### [Meta Reinforcement Learning]()



* __Generalizing Skills with Semi-Supervised Reinforcement Learning__, (2017), _Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine_. [[pdf]](https://arxiv.org/pdf/1612.00429.pdf) [[code]](https://github.com/cbfinn/gps/tree/ssrl)



* __Guided Meta-Policy Search__, (2019), _Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn_. [[pdf]](https://arxiv.org/pdf/1904.00956.pdf) [[code]](https://github.com/RussellM2020/GMPS)



* __End-to-End Robotic Reinforcement Learning without Reward Engineering__, (2019), _Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine_. [[pdf]](https://arxiv.org/abs/1904.07854) [[code]](https://github.com/avisingh599/reward-learning-rl)



* __Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables__, (2019), _Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine_. [[pdf]](https://arxiv.org/pdf/1903.08254) [[code]](https://github.com/katerakelly/oyster)



* __Meta-Gradient Reinforcement Learning__, (2018), _Zhongwen Xu, Hado van Hasselt,David Silver_. [[pdf]](http://papers.nips.cc/paper/7507-meta-gradient-reinforcement-learning.pdf)



* __Task-Agnostic Dynamics Priors for Deep Reinforcement Learning__, (2019), _Yilun Du, Karthik Narasimhan_. [[pdf]](https://arxiv.org/pdf/1905.04819.pdf)



* __Meta Reinforcement Learning with Task Embedding and Shared Policy__,(2019), _Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang_. [[pdf]](https://arxiv.org/pdf/1905.06527.pdf) 



* __NoRML: No-Reward Meta Learning__, (2019), _Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn_. [[pdf]](https://arxiv.org/pdf/1903.01063.pdf)



* __Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning__, (2019), _Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Prabuchandran K. J., Shalabh Bhatnagar_. [[pdf]](https://arxiv.org/pdf/1905.02907.pdf)



* __Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning__, (2019), _Brian Gaudet, Richard Linares, Roberto Furfaro_. [[pdf]](https://arxiv.org/pdf/1904.09865.pdf)



* __Watch, Try, Learn: Meta-Learning from Demonstrations and Reward__, (2019), _Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn_. [[pdf]](https://arxiv.org/pdf/1906.03352.pdf)



* __Options as responses: Grounding behavioural hierarchies in multi-agent RL__, (2019), _Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond, Joel Z. Leibo_. [[pdf]](https://arxiv.org/pdf/1906.01470.pdf)



* __Learning latent state representation for speeding up exploration__, (2019), _Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel_. [[pdf]](https://arxiv.org/pdf/1905.12621.pdf)



* __Beyond Exponentially Discounted Sum: Automatic Learning of Return Function__, (2019), _Yufei Wang, Qiwei Ye, Tie-Yan Liu_. [[pdf]](https://arxiv.org/pdf/1905.11591.pdf)



* __Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy__, (2019),  _Ruihan Yang, Qiwei Ye, Tie-Yan Liu_. [[pdf]](https://arxiv.org/pdf/1905.11583.pdf)



* __Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning__, (2019), _Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht_. [[pdf]](https://arxiv.org/pdf/1906.04737.pdf)



* __Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning__, (2019), _Yufei Wang, Ziju Shen, Zichao Long, Bin Dong_. [[pdf]](https://arxiv.org/pdf/1905.11079.pdf)



## [Books]()



* __Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow__, (2019), _Sudharsan Ravichandiran_. [[pdf]](https://www.amazon.com/Hands-Meta-Learning-Python-TensorFlow-ebook/dp/B07KJJHYKF/ref=tmm_kin_swatch_0?_encoding=UTF8&qid=&sr=) [[code]](https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python)



## Libraries



* [Higher by Facebook research](https://github.com/facebookresearch/higher)

* [TorchMeta](https://github.com/tristandeleu/pytorch-meta)

* [Learn2learn]( https://github.com/learnables/learn2learn)



## Blogs



* [Berkeley Artificial Intelligence Research blog](https://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/)



* [Meta-Learning: Learning to Learn Fast](https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html)



* [Meta-Reinforcement Learning](https://blog.floydhub.com/meta-rl/)



* [How to train your MAML: A step by step approach](https://www.bayeswatch.com/2018/11/30/HTYM/)



* [An Introduction to Meta-Learning](https://medium.com/walmartlabs/an-introduction-to-meta-learning-ced7072b80e7)



* [From zero to research — An introduction to Meta-learning](https://medium.com/huggingface/from-zero-to-research-an-introduction-to-meta-learning-8e16e677f78a)



* [What’s New in Deep Learning Research: Understanding Meta-Learning](https://towardsdatascience.com/whats-new-in-deep-learning-research-understanding-meta-learning-91fef1295660) 



* [Meta Reinforcement Learning Blog by Lilian Weng](https://lilianweng.github.io/lil-log/2019/06/23/meta-reinforcement-learning.html)



## Lecture Videos 



* [Stanford CS330: Multi-Task and Meta-Learning, 2019 by Chelsea Finn](https://youtu.be/0rZtSwNOTQo)



* [Meta Learning lecture by Soheil Feizi](https://www.youtube.com/watch?v=CRHKgOYXVe8)



* [Chelsea Finn: Building Unsupervised Versatile Agents with Meta-Learning](https://www.youtube.com/watch?v=i05Fk4ebMY0)



* [Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data](https://www.youtube.com/watch?v=NpSpHlHpz6k)



* [Model Agnostic Meta Learning by Siavash Khodadadeh](https://www.youtube.com/watch?v=wT45v8sIMDM)



* [Meta Learning by Siraj Raval](https://www.youtube.com/watch?v=2z0ofe2lpz4)



* [Meta Learning by Hugo Larochelle](https://www.youtube.com/watch?v=lz0ekIVfoFs) 



* [Meta Learning and One-Shot Learning](https://www.youtube.com/watch?v=KUWywwvQv8E)



## Datasets



Most popularly used datasets:



* [Omniglot](https://github.com/brendenlake/omniglot) 

* [mini-ImageNet](https://github.com/y2l/mini-imagenet-tools) 

* [ILSVRC](http://image-net.org/challenges/LSVRC/)

* [FGVC aircraft](http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/)

* [Caltech-UCSD Birds-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)



Check several other datasets by Google [here.](https://github.com/google-research/meta-dataset)



## Workshops



* [MetaLearn 2017](http://metalearning.ml/2017/)

* [MetaLearn 2018](http://metalearning.ml/2018/)

* [MetaLearn 2019](http://metalearning.ml/2019/)

* [MetaLearn 2020](https://meta-learn.github.io/2020/)



## Researchers



* [Chelsea Finn](http://people.eecs.berkeley.edu/~cbfinn/), _UC Berkeley_

* [Pieter Abbeel](https://people.eecs.berkeley.edu/~pabbeel/), _UC Berkeley_

* [Erin Grant](https://people.eecs.berkeley.edu/~eringrant/),  _UC Berkeley_

* [Raia Hadsell](http://raiahadsell.com/index.html), _DeepMind_

* [Misha Denil](http://mdenil.com/), _DeepMind_

* [Adam Santoro](https://scholar.google.com/citations?hl=en&user=evIkDWoAAAAJ&view_op=list_works&sortby=pubdate), _DeepMind_

* [Sachin Ravi](http://www.cs.princeton.edu/~sachinr/), _Princeton University_

* [David Abel](https://david-abel.github.io/), _Brown University_

* [Brenden Lake](https://cims.nyu.edu/~brenden/), _Facebook AI Research_



## Contributions



Contributions are most welcome, if you have any suggestions and improvements, please create an issue or raise a pull request.