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

Table of Contents

• Papers and Code
• Books
• Libraries
• Blogs
• Lecture Videos
• Datasets
• Workshops
• Researchers

Check out my Deep Reinforcement Learning Repo here.

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] [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

• Higher by Facebook research
• TorchMeta
• Learn2learn

Blogs

• Berkeley Artificial Intelligence Research blog

• Meta-Learning: Learning to Learn Fast

• Meta-Reinforcement Learning

• How to train your MAML: A step by step approach

• An Introduction to Meta-Learning

• From zero to research — An introduction to Meta-learning

• What’s New in Deep Learning Research: Understanding Meta-Learning

• Meta Reinforcement Learning Blog by Lilian Weng

Lecture Videos

• Stanford CS330: Multi-Task and Meta-Learning, 2019 by Chelsea Finn

• Meta Learning lecture by Soheil Feizi

• Chelsea Finn: Building Unsupervised Versatile Agents with Meta-Learning

• Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data

• Model Agnostic Meta Learning by Siavash Khodadadeh

• Meta Learning by Siraj Raval

• Meta Learning by Hugo Larochelle

• Meta Learning and One-Shot Learning

Datasets

Most popularly used datasets:

• Omniglot
• mini-ImageNet
• ILSVRC
• FGVC aircraft
• Caltech-UCSD Birds-200-2011

Check several other datasets by Google here.

Workshops

• MetaLearn 2017
• MetaLearn 2018
• MetaLearn 2019
• MetaLearn 2020

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

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