A curated list of Meta-Learning resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search.
Please feel free to pull requests or open an issue to add papers.
- Gradient Agreement as an Optimization Objective for Meta-Learning. Amir Erfan Eshratifar, David Eigen, Massoud Pedram.
- Few-Shot Image Recognition by Predicting Parameters from Activations. Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille. CVPR 2018.
- META-LEARNING WITH LATENT EMBEDDING OPTIMIZATION. Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero & Raia Hadsell
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine. ICML 2017.
- Prototypical Networks for Few-shot Learning, Jake Snell, Kevin Swersky, Richard S. Zemel. NIPS 2017.
- Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
- Learning to Learn without Gradient Descent by Gradient Descent, Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando de Freitas, ICML 2017
- OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING, Sachin Ravi, Hugo Larochelle. ICLR 2017 Torch Pytorch
- Meta-SGD: Learning to Learn Quickly for Few-Shot Learning, Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li Tensorflow
- Learning to Compare: Relation Network for Few-Shot Learning, Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales, CVPR 2018 Few-shot Pytorch Zero-shot Pytorch miniImageNet Pytorch
- Object-Level Representation Learning for Few-Shot Image Classification, Liangqu Long, Wei Wang, Jun Wen, Meihui Zhang, Qian Lin, Beng Chin Ooi
- A Simple Neural Attentive Meta-Learner, Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. ICLR 2018
- Meta-Learning for Semi-Supervised Few-Shot Classification, Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel. ICLR 2018
- Learning to Optimize, Ke Li, Jitendra Malik
- Matching Networks for One Shot Learning, Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
- Meta-Learning with Memory-Augmented Neural Networks, Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap Theano
- CAML: Fast Context Adaptation via Meta-Learning, Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson
- Unsupervised Learning via Meta-Learning, Kyle Hsu, Sergey Levine, Chelsea Finn MAML Tensorflow ProtoNets Tensorflow
- Fast Parameter Adaptation for Few-shot Image Captioning and Visual Question Answering. Xuanyi Dong, Linchao Zhu, De Zhang, Yi Yang, Fei Wu.
- From zero to research — An introduction to Meta-learning
- Deep learning to learn. Pieter Abbeel
- Meta-Learning Frontiers: Universal, Uncertain, and Unsupervised, Sergey Levine, Chelsea Finn
- Chelsa Finn, UC Berkeley
- Misha Denil, DeepMind
- Sachin Ravi, Princeton University
- Hugo Larochelle, Google Brain
- Jake Snell, University of Toronto, Vector Institute
- Adam Santoro, DeepMind
- JANE X. WANG, DeepMind