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Episodic learning is a popular practice among researchers and practitioners interested in few-shot learning.
Shift of bias for inductive concept learning
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Low data drug discovery with one-shot learning
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Model-agnostic meta-learning for fast adaptation of deep networks
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Optimization as a model for few-shot learning
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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Few-shot learning through an information retrieval lens
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Born again neural networks
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Rapid adaptation with conditionally shifted neurons
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Tadam: Task dependent adaptive metric for improved few-shot learning
B. Oreshkin, P. R. López, and A. Lacoste · 2018
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J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang · 2019
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A theoretical analysis of the number of shots in few-shot learning
T. Cao, M. Law, and S. Fidler · 2020
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A closer look at the training strategy for modern meta-learning
J. Chen, X.-M. Wu, Y. Li, Q. Li, L.-M. Zhan, and F.-l. Chung · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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A new meta-baseline for few-shot learning
Y. Chen, X. Wang, Z. Liu, H. Xu, and T. Darrell · 2020
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Low-shot learning with imprinted weights
H. Qi, M. Brown, and D. G. Lowe · 2018
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Meta-learning for semi-supervised few-shot classification
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel · 2018
Cited alongside, same era.
Infinite mixture prototypes for few-shot learning
K. R. Allen, E. Shelhamer, H. Shin, and J. B. Tenenbaum · 2019
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. H. Torr, and A. Vedaldi · 2019
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A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
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Analyzing and improving representations with the soft nearest neighbor loss
N. Frosst, N. Papernot, and G. Hinton · 2019
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A baseline for few-shot image classification
G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto · 2020
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Unraveling meta-learning: Understanding feature representations for few-shot tasks
M. Goldblum, S. Reich, L. Fowl, R. Ni, V. Cherepanova, and T. Goldstein · 2020
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Meta-learning in neural networks: A survey
T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey · 2020
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Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
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Bayesian meta-learning for the few-shot setting via deep kernels
M. Patacchiola, J. Turner, E. J. Crowley, M. O’Boyle, and A. J. Storkey · 2020
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola · 2020
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How important is the train-validation split in meta-learning?
Y. Bai, M. Chen, P. Zhou, T. Zhao, J. Lee, S. Kakade, H. Wang, and C. Xiong · 2021
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Melr: Meta-learning via modeling episode-level relationships for few-shot learning
N. Fei, Z. Lu, T. Xiang, and S. Huang · 2021
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Stanford cs330: Multi-task and meta-learning, 2019 | lecture 4 - non-parametric meta-learners
C. Finn · 2021
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Circumpapillary oct-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
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Understanding autism: the power of eeg harnessed by prototypical learning
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Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes
J. Snell and R. Zemel · 2021
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