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

Papers and code

A curated set of papers along with code.

Zero-Shot / One-Shot / Few-Shot / Learning

  • 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]
  • 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]

Blogs

Lecture Videos

Datasets

Most popularly used datasets:

Check several other datasets by Google here.

Workshops

Researchers

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