Awesome Meta Learning

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

Siamese Neural Networks for One-shot Image Recognition, (2015), Gregory Koch, Richard Zemel, Ruslan Salakhutdinov. [pdf] [code]
Prototypical Networks for Few-shot Learning, (2017), Jake Snell, Kevin Swersky, Richard S. Zemel. [pdf] [code]
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot (2017), Stanislav Fort. [pdf] [code]
Matching Networks for One Shot Learning, (2017), Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra. [pdf] [code]
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] [code]
One-shot Learning with Memory-Augmented Neural Networks, (2016), Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. [pdf] [code]
Optimization as a Model for Few-Shot Learning, (2016), Sachin Ravi and Hugo Larochelle. [pdf] [code]
An embarrassingly simple approach to zero-shot learning, (2015), B Romera-Paredes, Philip H. S. Torr. [pdf] [code]
Low-shot Learning by Shrinking and Hallucinating Features, (2017), Bharath Hariharan, Ross Girshick. [pdf] [code]
Low-shot learning with large-scale diffusion, (2018), Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou. [pdf] [code]
Low-Shot Learning with Imprinted Weights, (2018), Hang Qi, Matthew Brown, David G. Lowe. [pdf] [code]
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] [code]
One-Shot Learning for Semantic Segmentation, (2017), Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots. [pdf] [code]
Few-Shot Segmentation Propagation with Guided Networks, (2018), Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine. [pdf] [code]
Few-Shot Semantic Segmentation with Prototype Learning, (2018), Nanqing Dong and Eric P. Xing. [pdf]
Dynamic Few-Shot Visual Learning without Forgetting, (2018), Spyros Gidaris, Nikos Komodakis. [pdf] [code]
Feature Generating Networks for Zero-Shot Learning, (2017), Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata. [pdf]
Meta-Learning Deep Visual Words for Fast Video Object Segmentation, (2019), Harkirat Singh Behl, Mohammad Najafi, Anurag Arnab, Philip H.S. Torr. [pdf]

Model Agnostic Meta Learning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, (2017), Chelsea Finn, Pieter Abbeel, Sergey Levine. [pdf] [code]
Adversarial Meta-Learning, (2018), Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang. [pdf] [code]
On First-Order Meta-Learning Algorithms, (2018), Alex Nichol, Joshua Achiam, John Schulman. [pdf] [code]
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning, (2017), Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li. [pdf] [code]
Gradient Agreement as an Optimization Objective for Meta-Learning, (2018), Amir Erfan Eshratifar, David Eigen, Massoud Pedram. [pdf] [code]
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace, (2018), Yoonho Lee, Seungjin Choi. [pdf] [code]
A Simple Neural Attentive Meta-Learner, (2018), Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. [pdf] [code]
Personalizing Dialogue Agents via Meta-Learning, (2019), Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung. [pdf] [code]
How to train your MAML, (2019), Antreas Antoniou, Harrison Edwards, Amos Storkey. [pdf] [code]
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] [code]
Unsupervised Learning via Meta-Learning, (2019), Kyle Hsu, Sergey Levine, Chelsea Finn. [pdf] [code]
Few-Shot Image Recognition by Predicting Parameters from Activations, (2018), Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille. [pdf] [code]
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] [code]
MetaGAN: An Adversarial Approach to Few-Shot Learning, (2018), ZHANG, Ruixiang and Che, Tong and Ghahramani, Zoubin and Bengio, Yoshua and Song, Yangqiu. [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]
CAML: Fast Context Adaptation via Meta-Learning, (2019), Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson. [pdf]
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems, (2019), Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings. [pdf]
MIND: Model Independent Neural Decoder, (2019), Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan. [pdf]
Toward Multimodal Model-Agnostic Meta-Learning, (2018), Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim. [pdf]
Alpha MAML: Adaptive Model-Agnostic Meta-Learning, (2019), Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr. [pdf]
Online Meta-Learning, (2019), Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine. [pdf]

Meta Reinforcement Learning

Generalizing Skills with Semi-Supervised Reinforcement Learning, (2017), Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine. [pdf] [code]
Guided Meta-Policy Search, (2019), Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn. [pdf] [code]
End-to-End Robotic Reinforcement Learning without Reward Engineering, (2019), Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine. [pdf] [code]
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables, (2019), Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine. [pdf] [code]
Meta-Gradient Reinforcement Learning, (2018), Zhongwen Xu, Hado van Hasselt,David Silver. [pdf]
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning, (2019), Yilun Du, Karthik Narasimhan. [pdf]
Meta Reinforcement Learning with Task Embedding and Shared Policy,(2019), Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang. [pdf]
NoRML: No-Reward Meta Learning, (2019), Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn. [pdf]
Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning, (2019), Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Prabuchandran K. J., Shalabh Bhatnagar. [pdf]
Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning, (2019), Brian Gaudet, Richard Linares, Roberto Furfaro. [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]
Options as responses: Grounding behavioural hierarchies in multi-agent RL, (2019), Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond, Joel Z. Leibo. [pdf]
Learning latent state representation for speeding up exploration, (2019), Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel. [pdf]
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function, (2019), Yufei Wang, Qiwei Ye, Tie-Yan Liu. [pdf]
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy, (2019), Ruihan Yang, Qiwei Ye, Tie-Yan Liu. [pdf]
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning, (2019), Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht. [pdf]
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning, (2019), Yufei Wang, Ziju Shen, Zichao Long, Bin Dong. [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] [code]

Libraries

Blogs

Lecture Videos

Datasets

Most popularly used datasets:

Check several other datasets by Google here.

Workshops

Researchers

Chelsea Finn, UC Berkeley
Pieter Abbeel, UC Berkeley
Erin Grant, UC Berkeley
Raia Hadsell, DeepMind
Misha Denil, DeepMind
Adam Santoro, DeepMind
Sachin Ravi, Princeton University
David Abel, Brown University
Brenden Lake, Facebook AI Research

Contributions

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

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