【论文整理】meta-learning
meta-learning
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.
Papers and Code
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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle.
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Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace. Yoonho Lee, Seungjin Choi.
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FIGR: Few-shot Image Generation with Reptile. Louis Clouâtre, Marc Demers.
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Online gradient-based mixtures for transfer modulation in meta-learning. Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine Heller.
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Auto-Meta: Automated Gradient Based Meta Learner Search. Jaehong Kim, Youngduck Choi, Moonsu Cha, Jung Kwon Lee, Sangyeul Lee, Sungwan Kim, Yongseok Choi, Jiwon Kim.
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MetaGAN: An Adversarial Approach to Few-Shot Learning. ZHANG, Ruixiang and Che, Tong and Ghahramani, Zoubin and Bengio, Yoshua and Song, Yangqiu.
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Learned Optimizers that Scale and Generalize. Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein.
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Guiding Policies with Language via Meta-Learning. John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, John DeNero, Pieter Abbeel, Sergey Levine.
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Deep Comparison: Relation Columns for Few-Shot Learning. Xueting Zhang, Flood Sung, Yuting Qiang, Yongxin Yang, Timothy M. Hospedales.
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Towards learning-to-learn. Benjamin James Lansdell, Konrad Paul Kording.
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Learning to Learn with Gradients. Finn, Chelsea.
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How to train your MAML. Antreas Antoniou, Harrison Edwards, Amos Storkey.
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Learned optimizers that outperform SGD on wall-clock and validation loss. Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
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Gradient Agreement as an Optimization Objective for Meta-Learning. Amir Erfan Eshratifar, David Eigen, Massoud Pedram.
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Few-Shot Image Recognition by Predicting Parameters from Activations. Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille. CVPR 2018.
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META-LEARNING WITH LATENT EMBEDDING OPTIMIZATION. Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero & Raia Hadsell
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chelsea Finn, Pieter Abbeel, Sergey Levine. ICML 2017.
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On First-Order Meta-Learning Algorithms. Alex Nichol, Joshua Achiam, John Schulman.
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Prototypical Networks for Few-shot Learning, Jake Snell, Kevin Swersky, Richard S. Zemel. NIPS 2017.
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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
(github.jpg)]](https://github.com/deepmind/learning-to-learn)
(github.jpg)]](https://becominghuman.ai/paper-repro-learning-to-learn-by-gradient-descent-by-gradient-descent-6e504cc1c0de) -
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
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Meta-SGD: Learning to Learn Quickly for Few-Shot Learning, Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li
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Unsupervised Meta-Learning for Reinforcement Learning. Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine.
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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[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-ciyXQ0sT-1576313684532)(github.jpg)]
Zero-shot Pytorch[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-fmfpwIPF-1576313684532)(github.jpg)]
miniImageNet Pytorch[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-fh2szE8z-1576313684533)(github.jpg)] -
Object-Level Representation Learning for Few-Shot Image Classification, Liangqu Long, Wei Wang, Jun Wen, Meihui Zhang, Qian Lin, Beng Chin Ooi
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A Simple Neural Attentive Meta-Learner, Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. ICLR 2018
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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
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Learning to Optimize, Ke Li, Jitendra Malik
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Matching Networks for One Shot Learning, Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
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Meta-Learning with Memory-Augmented Neural Networks, Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
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CAML: Fast Context Adaptation via Meta-Learning, Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson
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Unsupervised Learning via Meta-Learning, Kyle Hsu, Sergey Levine, Chelsea Finn
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Fast Parameter Adaptation for Few-shot Image Captioning and Visual Question Answering. Xuanyi Dong, Linchao Zhu, De Zhang, Yi Yang, Fei Wu. [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-VybKZBfx-1576313684534)(github.jpg)]
Tutorials and Slides
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NeuraIPS meta-learning workshop: 2018, 2017
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What’s Wrong with Meta-Learning
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Meta-Learning: Learning to Learn Fast
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How to train your MAML: A step by step approach
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From zero to research — An introduction to Meta-learning
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Deep learning to learn. Pieter Abbeel
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Meta-Learning Frontiers: Universal, Uncertain, and Unsupervised, Sergey Levine, Chelsea Finn
Reseachers and Labs
- 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