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Few-shot classification is the task of predicting the category of an example from a set of few labeled examples.
Infinite mixture prototypes for few-shot learning
Kelsey R. Allen, Evan Shelhamer, Hanul Shin, and Joshua B. Tenenbaum · 1902
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An algorithm for finding intrinsic dimensionality of data
Keinosuke Fukunaga and David R. Olsen · 1971
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Introduction to Statistical Pattern Recognition (2Nd Ed.)
Keinosuke Fukunaga · 1990
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Measuring the vc-dimension of a learning machine
Vladimir Vapnik, Esther Levin, and Yann Le Cun · 1994
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Statistical Machine Learning
Vladimir N. Vapnik · 1998
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Linear Models in Statistics
Alvin C. Rencher and G. Bruce Schaalje · 2008
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 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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Fast learning with weak synaptic plasticity
Pierre Yger, Marcel Stimberg, and Romain Brette · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Closed-form training of mahalanobis distance for supervised clustering
Marc T. Law, Yaoliang Yu, Matthieu Cord, and Eric P. Xing · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Matching networks for one shot learning
Few-shot semantic segmentation with prototype learning
Nanqing Dong and Eric P. Xing · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 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, and Richard S. Zemel · 2018
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Learning embedding adaptation for few-shot learning
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha · 2018
Later among the works it cites.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Meta-learning by adjusting priors based on extended pac-bayes theory
Ron Amit and Ron Meir · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni
Cited in the paper.
Dimensionality reduction for representing the knowledge of probabilistic models
Marc T. Law, Jake Snell, Amir massoud Farahmand, Raquel Urtasun, and Richard S. Zemel · 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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Video face clustering with unknown number of clusters
Makarand Tapaswi, Marc T. Law, and Sanja Fidler · 2019
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Centroid-based deep metric learning for speaker recognition
Jixuan Wang, Kuan-Chieh Wang, Marc T. Law, Frank Rudzicz, and Michael Brudno · 2019
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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, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2020
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