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Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning algorithms nowadays.
Bagging predictors
Leo Breiman · 1996
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A model of inductive bias learning
Jonathan Baxter · 2000
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Transfer bounds for linear feature learning
Andreas Maurer · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Metalearning: a survey of trends and technologies
Christiane Lemke, Marcin Budka, and Bogdan Gabrys · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning visual features from large weakly supervised data
Armand Joulin, Laurens Van Der Maaten, Allan Jabri, and Nicolas Vasilache · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Learning to learn: Model regression networks for easy small sample learning
Yu-Xiong Wang and Martial Hebert · 2016
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Learning algorithms for active learning
Philip Bachman, Alessandro Sordoni, and Adam Trischler · 2017
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Neural optimizer search with reinforcement learning
Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le · 2017
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Logical vision: One-shot meta-interpretive learning from real images
Wang-Zhou Dai, Stephen Muggleton, Jing Wen, Alireza Tamaddoni-Nezhad, and Zhi-Hua Zhou · 2017
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Towards a neural statistician
Harrison Edwards and Amos Storkey · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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A bridge between hyperparameter optimization and larning-to-learn
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
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Low-shot visual recognition by shrinking and hallucinating features
Bharath Hariharan and Ross B. Girshick · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Learning to remember rare events
Łukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio · 2017
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Learning to optimize
Ke Li and Jitendra Malik · 2017
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Few-shot adversarial domain adaptation
Saeid Motiian, Quinn Jones, Seyed Mehdi Iranmanesh, and Gianfranco Doretto · 2017
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Learning to compose domain-specific transformations for data augmentation
Alexander J Ratner, Henry Ehrenberg, Zeshan Hussain, Jared Dunnmon, and Christopher Ré · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Attentive recurrent comparators
Pranav Shyam, Shubham Gupta, and Ambedkar Dukkipati · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
Cited alongside, same era.
A meta-learning perspective on cold-start recommendations for items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle · 2017
Cited alongside, same era.
Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, and Jascha Sohl-Dickstein · 2017
Unsupervised learning via meta-learning
Kyle Hsu, Sergey Levine, and Chelsea Finn · 2019
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Feature-critic networks for heterogeneous domain generalization
Yiying Li, Yongxin Yang, Wei Zhou, and Timothy M Hospedales · 2019
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Meta-learning update rules for unsupervised representation learning
Luke Metz, Niru Maheswaranathan, Brian Cheung, and Jascha Sohl-Dickstein · 2019
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Tunability: Importance of hyperparameters of machine learning algorithms
Philipp Probst, Anne-Laure Boulesteix, and Bernd Bischl · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, and Sergey Levine · 2019
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Cited alongside, same era.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2018
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Cited alongside, same era.
Learning to learn around A common mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, and Massimiliano Pontil · 2018
Cited alongside, same era.
Learning to teach
Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, and Tie-Yan Liu · 2018
Cited alongside, same era.
Learning to Learn with Gradients
Chelsea Finn · 2018
Cited alongside, same era.
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
Cited alongside, same era.
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Fast and flexible multi-task classification using conditional neural adaptive processes
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, and Richard E. Turner · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2019
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Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
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Meta-transfer learning through hard tasks
Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, and Bernt Schiele · 2019
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Multimodal model-agnostic meta-learning via task-aware modulation
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, and Joseph J. Lim · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Yan Wang, Wei-Lun Chao, Kilian Q Weinberger, and Laurens van der Maaten · 2019
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PARN: position-aware relation networks for few-shot learning
Ziyang Wu, Yuwei Li, Lihua Guo, and Kui Jia · 2019
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Hierarchically structured meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang, and Zhenhui Li · 2019
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Associative alignment for few-shot image classification
Arman Afrasiyabi, Jean-François Lalonde, and Christian Gagné · 2020
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MATE: plugging in model awareness to task embedding for meta learning
Xiaohan Chen, Zhangyang Wang, Siyu Tang, and Krikamol Muandet · 2020
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A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
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Meta-learning in neural networks: A survey
Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J. Storkey · 2020
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Adversarial feature hallucination networks for few-shot learning
Kai Li, Yulun Zhang, Kunpeng Li, and Yun Fu · 2020
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Negative margin matters: Understanding margin in few-shot classification
Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu · 2020
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2020
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Meta-learning requires meta-augmentation
Janarthanan Rajendran, Alex Irpan, and Eric Jang · 2020
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Adaptive subspaces for few-shot learning
Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi · 2020
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Rethinking few-shot image classification: A good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola · 2020
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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, and Hugo Larochelle · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
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Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2020
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Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers
Chi Zhang, Yujun Cai, Guosheng Lin, and Chunhua Shen · 2020
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Embedding adaptation is still needed for few-shot learning
Sébastien M. R. Arnold and Fei Sha · 2021
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Meta-learning with negative learning rates
Alberto Bernacchia · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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MELR: meta-learning via modeling episode-level relationships for few-shot learning
Nanyi Fei, Zhiwu Lu, Tao Xiang, and Songfang Huang · 2021
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Large-scale meta-learning with continual trajectory shifting
Jaewoong Shin, Hae Beom Lee, Boqing Gong, and Sung Ju Hwang · 2021
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Improving generalization in meta-learning via task augmentation
Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, et al · 2021
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Few-shot learning with a strong teacher
Han-Jia Ye, Lu Ming, De-Chuan Zhan, and Wei-Lun Chao · 2021
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