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We show that several popular few-shot learning benchmarks can be solved with varying degrees of success without using support set Labels at Test-time (LT).
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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Y.-C. Hsu, Z. Lv, and Z. Kira · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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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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Cross attention network for few-shot classification
R. Hou, H. Chang, M. Bingpeng, S. Shan, and X. Chen · 2019
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Multi-class classification without multi-class labels
Y.-C. Hsu, Z. Lv, J. Schlosser, P. Odom, and Z. Kira · 2019
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Meta-learning with differentiable convex optimization
K. Lee, S. Maji, A. Ravichandran, and S. Soatto · 2019
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Finding task-relevant features for few-shot learning by category traversal
H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang · 2019
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Transductive episodic-wise adaptive metric for few-shot learning
L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian · 2019
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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A. Genevay, G. Peyre, and M. Cuturi · 2018
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Unsupervised learning via meta-learning
K. Hsu, S. Levine, and C. Finn · 2018
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S. Khodadadeh, L. Bölöni, and M. Shah · 2018
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Learning to propagate labels: Transductive propagation network for few-shot learning
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
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Fast and flexible multi-task classification using conditional neural adaptive processes
J. Requeima, J. Gordon, J. Bronskill, S. Nowozin, and R. E. Turner · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Y. Wang, W.-L. Chao, K. Q. Weinberger, and L. van der Maaten · 2019
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Meta-learning without memorization
M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn · 2019
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Selecting relevant features from a universal representation for few-shot classification
N. Dvornik, C. Schmid, and J. Mairal · 2020
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Negative margin matters: Understanding margin in few-shot classification
B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu · 2020
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Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, and H. Larochelle · 2020
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Cross-domain few-shot classification via learned feature-wise transformation
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha · 2020
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