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Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes.
Pearson correlation coefficient
J. Benesty, J. Chen, Y. Huang, and I. Cohen · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization, 2014
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. P. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning, 2017
J. Snell, K. Swersky, and R. S. Zemel · 2017
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp, 2019
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P. Manzagol, and H. Larochelle · 2019
Earlier work this paper cites.
The visual task adaptation benchmark
X. Zhai, J. Puigcerver, A. Kolesnikov, P. Ruyssen, C. Riquelme, M. Lucic, J. Djolonga, A. S. Pinto, M. Neumann, A. Dosovitskiy, L. Beyer, O. Bachem, M. Tschannen, M. Michalski, O. Bousquet, S. Gelly, and N. Houlsby · 2019
Earlier work this paper cites.
A new meta-baseline for few-shot learning
Y. Chen, X. Wang, Z. Liu, H. Xu, and T. Darrell · 2020
Earlier work this paper cites.
Meta-learning in neural networks: A survey
T. M. Hospedales, A. Antoniou, P. Micaelli, and A. J. Storkey · 2020
Cited alongside, same era.
Adapterfusion: Non-destructive task composition for transfer learning
J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych · 2020
Cited alongside, same era.
Wandering within a world: Online contextualized few-shot learning
M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. S. Zemel · 2020
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou · 2020
Cited alongside, same era.
Memory efficient meta-learning with large images, 2021
Orbit: A real-world few-shot dataset for teachable object recognition collected from people who are blind or low vision, 2021
D. Massiceti, L. Theodorou, L. Zintgraf, M. T. Harris, S. Stumpf, C. Morrison, E. Cutrell, and K. Hofmann · 2021
Later among the works it cites.
FS-mol: A few-shot learning dataset of molecules
M. Stanley, J. F. Bronskill, K. Maziarz, H. Misztela, J. Lanini, M. Segler, N. Schneider, and M. Brockschmidt · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
E. B. Zaken, S. Ravfogel, and Y. Goldberg · 2021
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Self-promoted supervision for few-shot transformer, 2022
B. Dong, P. Zhou, S. Yan, and W. Zuo · 2022
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Rethinking generalization in few-shot classification
M. Hiller, R. Ma, M. Harandi, and T. Drummond · 2022
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J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. E. Turner · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, and W. Chen · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Cited alongside, same era.
Cross-domain few-shot learning with task-specific adapters, 2021
W.-H. Li, X. Liu, and H. Bilen · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
Cited alongside, same era.
Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference, 2022
S. X. Hu, D. Li, J. Stühmer, M. Kim, and T. M. Hospedales · 2022
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Visual prompt tuning, 2022
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim · 2022
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Fit: Parameter efficient few-shot transfer learning for personalized and federated image classification, 2022
A. Shysheya, J. Bronskill, M. Patacchiola, S. Nowozin, and R. E. Turner · 2022
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Lst: Ladder side-tuning for parameter and memory efficient transfer learning, 2022
Y.-L. Sung, J. Cho, and M. Bansal · 2022
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Exploring efficient few-shot adaptation for vision transformers
C. Xu, S. Yang, Y. Wang, Z. Wang, Y. Fu, and X. Xue · 2022
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