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Deep Neural Networks (DNNs) often struggle with one-shot learning where we have only one or a few labeled training examples per category.
Linear smoothers and additive models
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Taxonomic prediction with tree-structured covariances
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Distributed representations of words and phrases and their compositionality
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Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S. (2014) · 2014
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Glove: Global vectors for word representation
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How transferable are features in deep neural networks?
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Attribute-based classification for zero-shot visual object categorization
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Simonyan, K. and Zisserman, A. (2014) · 2014
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Siamese neural networks for one-shot image recognition
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Adam: A method for stochastic optimization
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He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016) · 2016
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Incorporating side information into recurrent neural network language models
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One-shot learning with memory-augmented neural networks
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
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