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Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task.
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The Caltech-UCSD birds-200-2011 dataset
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep learning
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ImageNet large scale visual recognition challenge
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Deep residual learning for image recognition
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Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T. (2018) · 2018
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Meta-learning priors for efficient online bayesian regression
Harrison, J., Sharma, A., and Pavone, M. (2018) · 2018
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Few-shot learning with metric-agnostic conditional embeddings
Hilliard, N., Phillips, L., Howland, S., Yankov, A., Corley, C. D., and Hodas, N. O. (2018) · 2018
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A simple neural attentive meta-learner
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Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A. (2018) · 2018
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
Cited alongside, same era.
Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P. (2016) · 2016
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EMNIST: an extension of MNIST to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A. (2017) · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W. (2017) · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017) · 2017
Cited alongside, same era.
Few-shot image recognition by predicting parameters from activations
Qiao, S., Liu, C., Shen, W., and Yuille, A. L. (2018) · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M. (2018) · 2018
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Learning embedding adaptation for few-shot learning
Ye, H.-J., Hu, H., Zhan, D.-C., and Sha, F. (2018) · 2018
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Bayesian model-agnostic meta-learning
Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., and Ahn, S. (2018) · 2018
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How to train your MAML
Antoniou, A., Edwards, H., and Storkey, A. (2019) · 2019
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Learning to learn by self-critique
Antoniou, A. and Storkey, A. (2019) · 2019
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Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A. (2019) · 2019
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A closer look at few-shot classification
Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C., and Huang, J.-B. (2019) · 2019
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Meta-learning probabilistic inference for prediction
Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., and Turner, R. (2019) · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
Jerfel, G., Grant, E., Griffiths, T., and Heller, K. A. (2019) · 2019
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Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S. M., and Levine, S. (2019) · 2019
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