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The field of few-shot learning has been laboriously explored in the supervised setting, where per-class labels are available.
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deep face recognition
Parkhi, O. M., Vedaldi, A., Zisserman, A., et al · 2015
Earlier work this paper cites.
Tutorial on variational autoencoders
Doersch, C · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2016
Earlier work this paper cites.
One-shot learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Cited alongside, same era.
Understanding data augmentation for classification: when to warp?
Wong, S. C., Gatt, A., Stamatescu, V., and McDonnell, M. D · 2016
Cited alongside, same era.
Unsupervised learning by predicting noise
Bojanowski, P. and Joulin, A · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Later among the works it cites.
Antoniou, A., Edwards, H., and Storkey, A · 2018
Later among the works it cites.
Deep clustering for unsupervised learning of visual features
Caron, M., Bojanowski, P., Joulin, A., and Douze, M · 2018
Later among the works it cites.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
Later among the works it cites.
Unsupervised learning via meta-learning
Hsu, K., Levine, S., and Finn, C · 2018
Later among the works it cites.
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Unsupervised meta-learning for few-shot image and video classification
Khodadadeh, S., Bölöni, L., and Shah, M · 2018
Later among the works it cites.
Semi-supervised few-shot learning with maml
Rinu Boney, A. I · 2018
Later among the works it cites.