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Meta-learning algorithms produce feature extractors which achieve state-of-the-art performance on few-shot classification.
Centroid networks for few-shot clustering and unsupervised few-shot classification
Huang, G., Larochelle, H., and Lacoste-Julien, S · 1902
Earlier work this paper cites.
Similarity of neural network representations revisited
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G · 1905
Earlier work this paper cites.
Understanding generalization through visualizations
Huang, W. R., Emam, Z., Goldblum, M., Fowl, L., Terry, J. K., Huang, F., and Goldstein, T · 1906
Earlier work this paper cites.
Robust few-shot learning with adversarially queried meta-learners
Goldblum, M., Fowl, L., and Goldstein, T · 1910
Earlier work this paper cites.
Fisher discriminant analysis with kernels
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.-R · 1999
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al · 2011
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Low-rank matrix factorization for deep neural network training with high-dimensional output targets
Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., and Ramabhadran, B · 2013
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.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Cited alongside, same era.
Low data drug discovery with one-shot learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., and Pande, V · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T · 2018
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A · 2018
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A closer look at few-shot classification
Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C. F., and Huang, J.-B · 2019
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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A., and Soatto, S · 2019
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Analyzing and improving representations with the soft nearest neighbor loss
Frosst, N., Papernot, N., and Hinton, G · 2019
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Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S · 2019
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Cited alongside, same era.
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., and Finn, C · 2018
Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Nichol, A. and Schulman, J · 2018
Cited alongside, same era.
Truth or backpropaganda? an empirical investigation of deep learning theory
Goldblum, M., Geiping, J., Schwarzschild, A., Moeller, M., and Goldstein, T
Cited in the paper.
Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V
Cited in the paper.
Fast and generalized adaptation for few-shot learning
Song, L., Liu, J., and Qin, Y · 2019
Later among the works it cites.
Pay attention to features, transfer learn faster CNNs
Wang, K., Gao, X., Zhao, Y., Li, X., Dou, D., and Xu, C.-Z · 2020
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