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We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning from one example through shared densities on transforms
Erik G Miller, Nicholas E Matsakis, and Paul A Viola · 2000
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Neighbourhood components analysis
Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis, and Ruslan Salakhutdinov · 2004
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Clustering with bregman divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
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Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger, John Blitzer, and Lawrence K Saul · 2005
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Learning a nonlinear embedding by preserving class neighbourhood structure
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2007
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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A deep non-linear feature mapping for large-margin knn classification
Renqiang Min, David A Stanley, Zineng Yuan, Anthony Bonner, and Zhaolei Zhang · 2009
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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One shot learning of simple visual concepts
Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum · 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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Metric learning: A survey
Brian Kulis · 2012
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Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
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A survey on metric learning for feature vectors and structured data
Aurélien Bellet, Amaury Habrard, and Marc Sebban · 2013
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Write a classifier: Zero-shot learning using purely textual descriptions
Mohamed Elhoseiny, Babak Saleh, and Ahmed Elgammal · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Distance-based image classification: Generalizing to new classes at near-zero cost
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Siamese neural networks for one-shot image recognition
Gregory Koch · 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 Bernstein, Alexander C. Berg, and Li Fei-Fei · 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 deep parsimonious representations
Renjie Liao, Alexander Schwing, Richard Zemel, and Raquel Urtasun · 2016
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Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
Jimmy Ba, Kevin Swersky, Sanja Fidler, and Ruslan Salakhutdinov · 2015
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Scott Reed, Zeynep Akata, Bernt Schiele, and Honglak Lee · 2016
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Metric learning with adaptive density discrimination
Oren Rippel, Manohar Paluri, Piotr Dollar, and Lubomir Bourdev · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
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Towards a neural statistician
Harrison Edwards and Amos Storkey · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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