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Few-shot learning (FSL) aims to generate a classifier using limited labeled examples.
H.-J. Ye, D.-C. Zhan, Y. Jiang, and Z.-H. Zhou, “Rectify heterogeneous models with semantic mapping,” in
1913
Earlier work this paper cites.
Z.-H. Zhou and Y. Jiang, “Nec4.5: Neural ensemble based C4.5,”
2004
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “One-shot learning of object categories,”
2006
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The Caltech-UCSD Birds-200-2011 Dataset,” California Institute of Technology, Tech. Rep. CNS-TR-2011-001, 2011
2011
Earlier work this paper cites.
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka, “Distance-based image classification: Generalizing to new classes at near-zero cost,”
2013
Earlier work this paper cites.
B. M. Lake, R. R. Salakhutdinov, and J. Tenenbaum, “One-shot learning by inverting a compositional causal process,” in
2013
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,”
2015
Earlier work this paper cites.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in
2015
Earlier work this paper cites.
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” in
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in
2015
Earlier work this paper cites.
B. Lake, T. D. Ullman, J. Tenenbaum, and S. Gershman, “Building machines that learn and think like people,”
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching networks for one shot learning,” in
2016
Earlier work this paper cites.
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas, “Learning to learn by gradient descent by gradient descent,” in
2016
Earlier work this paper cites.
Y.-X. Wang and M. Hebert, “Learning to learn: Model regression networks for easy small sample learning,” in
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi, “Learning feed-forward one-shot learners,” in
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Lopez-Paz, L. Bottou, B. Schölkopf, and V. Vapnik, “Unifying distillation and privileged information,” in
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” in
2016
Earlier work this paper cites.
S. Motiian, Q. Jones, S. M. Iranmanesh, and G. Doretto, “Few-shot adversarial domain adaptation,” in
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in
2017
Earlier work this paper cites.
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” in
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. S. Zemel, “Prototypical networks for few-shot learning,” in
2017
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in
2017
Earlier work this paper cites.
Y.-X. Wang, D. Ramanan, and M. Hebert, “Learning to model the tail,” in
2017
Earlier work this paper cites.
B. Hariharan and R. B. Girshick, “Low-shot visual recognition by shrinking and hallucinating features,” in
2017
Earlier work this paper cites.
E. Triantafillou, R. S. Zemel, and R. Urtasun, “Few-shot learning through an information retrieval lens,” in
2017
Earlier work this paper cites.
H. Edwards and A. Storkey, “Towards a neural statistician,” in
2017
Earlier work this paper cites.
Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-sgd: Learning to learn quickly for few shot learning,”
2017
Earlier work this paper cites.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in
2018
Earlier work this paper cites.
J. Gu, Y. Wang, Y. Chen, V. O. K. Li, and K. Cho, “Meta-learning for low-resource neural machine translation,” in
2018
Earlier work this paper cites.
M. Ren, W. Zeng, B. Yang, and R. Urtasun, “Learning to reweight examples for robust deep learning,” in
2018
Earlier work this paper cites.
L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. M. F. Moura, “Few-shot human motion prediction via meta-learning,” in
2018
Earlier work this paper cites.
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel, “Meta-learning for semi-supervised few-shot classification,” in
2018
Earlier work this paper cites.
B. N. Oreshkin, P. R. López, and A. Lacoste, “TADAM: task dependent adaptive metric for improved few-shot learning,” in
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Antoniou, A. Storkey, and H. Edwards, “Data augmentation generative adversarial networks,” in
2018
Cited alongside, same era.
T. R. Scott, K. Ridgeway, and M. C. Mozer, “Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning,” in
2018
Cited alongside, same era.
Z. Li and D. Hoiem, “Learning without forgetting,”
2018
Cited alongside, same era.
Y. Lee and S. Choi, “Gradient-based meta-learning with learned layerwise metric and subspace,” in
2018
Cited alongside, same era.
A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,”
2018
Cited alongside, same era.
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan, “Low-shot learning from imaginary data,” in
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in
2019
Later among the works it cites.
Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal, “Multi-level semantic feature augmentation for one-shot learning,”
2019
Later among the works it cites.
M. R. U. Saputra, P. P. B. de Gusmao, Y. Almalioglu, A. Markham, and N. Trigoni, “Distilling knowledge from a deep pose regressor network,” in
2019
Later among the works it cites.
2020
Later among the works it cites.
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le, “Self-training with noisy student improves imagenet classification,” in
2020
Later among the works it cites.
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2018
Cited alongside, same era.
S. Gidaris and N. Komodakis, “Dynamic few-shot visual learning without forgetting,” in
2018
Cited alongside, same era.
S. Qiao, C. Liu, W. Shen, and A. L. Yuille, “Few-shot image recognition by predicting parameters from activations,” in
2018
Cited alongside, same era.
D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in
2018
Cited alongside, same era.
T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born-again neural networks,” in
2018
Cited alongside, same era.
C. Finn and S. Levine, “Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm,” in
2018
Cited alongside, same era.
R. Boney and A. Ilin, “Semi-supervised few-shot learning with maml,” in
2018
Cited alongside, same era.
H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha, “Few-shot learning via embedding adaptation with set-to-set functions,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Zhang, D. Wang, and S. Gai, “Knowledge distillation for model-agnostic meta-learning,” in
2020
Later among the works it cites.
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola, “Rethinking few-shot image classification: A good embedding is all you need?” in
2020
Later among the works it cites.
C. Zhang, Y. Cai, G. Lin, and C. Shen, “Deepemd: Few-shot image classification with differentiable earth mover’s distance and structured classifiers,” in
2020
Later among the works it cites.
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. Hinton, “Big self-supervised models are strong semi-supervised learners,” in
2020
Later among the works it cites.
G. Xu, Z. Liu, X. Li, and C. C. Loy, “Knowledge distillation meets self-supervision,” in
2020
Later among the works it cites.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,”
2020
Later among the works it cites.
K. Li, Y. Zhang, K. Li, and Y. Fu, “Adversarial feature hallucination networks for few-shot learning,” in
2020
Later among the works it cites.
G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto, “A baseline for few-shot image classification,” in
2020
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J.-M. Pérez-Rúa, X. Zhu, T. M. Hospedales, and T. Xiang, “Incremental few-shot object detection,” in
2020
Later among the works it cites.
T. Cao, M. T. Law, and S. Fidler, “A theoretical analysis of the number of shots in few-shot learning,” in
2020
Later among the works it cites.
S.-I. Mirzadeh, M. Farajtabar, A. Li, N. Levine, A. Matsukawa, and H. Ghasemzadeh, “Improved knowledge distillation via teacher assistant,” in
2020
Later among the works it cites.
H.-J. Ye, S. Lu, and D.-C. Zhan, “Distilling cross-task knowledge via relationship matching,” in
2020
Later among the works it cites.
X. Tao, X. Chang, X. Hong, X. Wei, and Y. Gong, “Topology-preserving class-incremental learning,” in
2020
Later among the works it cites.
L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,”
2020
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2020
Later among the works it cites.
A. Li, W. Huang, X. Lan, J. Feng, Z. Li, and L. Wang, “Boosting few-shot learning with adaptive margin loss,” in
2020
Later among the works it cites.
C. Simon, P. Koniusz, R. Nock, and M. Harandi, “Adaptive subspaces for few-shot learning,” in
2020
Later among the works it cites.
Y. Liu, B. Schiele, and Q. Sun, “An ensemble of epoch-wise empirical bayes for few-shot learning,” in
2020
Later among the works it cites.
B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu, “Negative margin matters: Understanding margin in few-shot classification,” in
2020
Later among the works it cites.
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang, “Cross-domain few-shot classification via learned feature-wise transformation,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
——, “Heterogeneous few-shot model rectification with semantic mapping,”
2021
Closest in time.
M. Chen, X. Wang, H. Luo, Y. Geng, and W. Liu, “Learning to focus: cascaded feature matching network for few-shot image recognition,”
2021
Closest in time.
N. Pang, X. Zhao, W. Wang, W. Xiao, and D. Guo, “Few-shot text classification by leveraging bi-directional attention and cross-class knowledge,”
2021
Closest in time.
H.-J. Ye and W.-L. Chao, “How to train your MAML to excel in few-shot classification,”
2021
Closest in time.
X. Yue, Z. Zheng, S. Zhang, Y. Gao, T. Darrell, K. Keutzer, and A. L. Sangiovanni-Vincentelli, “Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation,” in
2021
Closest in time.
U. Ojha, Y. Li, J. Lu, A. A. Efros, Y. J. Lee, E. Shechtman, and R. Zhang, “Few-shot image generation via cross-domain correspondence,” in
2021
Closest in time.
C. Zhu, F. Chen, U. Ahmed, Z. Shen, and M. Savvides, “Semantic relation reasoning for shot-stable few-shot object detection,” in
2021
Closest in time.
H.-J. Ye, H. Hu, and D.-C. Zhan, “Learning adaptive classifiers synthesis for generalized few-shot learning,”
2021
Closest in time.
H. Yao, L.-K. Huang, L. Zhang, Y. Wei, L. Tian, J. Zou, J. Huang, and Z. Li, “Improving generalization in meta-learning via task augmentation,” in
2021
Closest in time.
E. Creager, J.-H. Jacobsen, and R. S. Zemel, “Environment inference for invariant learning,” in
2021
Closest in time.