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Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., et al. (2019) · 1903
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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) · 1904
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O. (2019) · 1909
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J. (1987) · 1987
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Meta-learning in neural networks: A survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A. (2020) · 2004
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The netflix prize
Bennett, J., Lanning, S., et al. (2007) · 2007
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Coordinate descent algorithms
Wright, S. J. (2015) · 2015
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A learned representation for artistic style
Dumoulin, V., Shlens, J., and Kudlur, M. (2016) · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
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Discriminative k-shot learning using probabilistic models
Bauer, M., Rojas-Carulla, M., Świątkowski, J. B., Schölkopf, B., and Turner, R. E. (2017) · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S. (2017) · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A. (2017) · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. (2017) · 2017
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The microsoft 2017 conversational speech recognition system
Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., and Stolcke, A. (2018) · 2017
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Antoniou, A., Edwards, H., and Storkey, A. (2018) · 2018
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A. (2018) · 2018
Cited alongside, same era.
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W. (2018) · 2018
Cited alongside, same era.
Meta-learning probabilistic inference for prediction
Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., and Turner, R. E. (2018) · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G. (2018) · 2018
Meta-transfer learning for few-shot learning
Sun, Q., Liu, Y., Chua, T.-S., and Schiele, B. (2019) · 2019
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Fast context adaptation via meta-learning
Zintgraf, L., Shiarli, K., Kurin, V., Hofmann, K., and Whiteson, S. (2019) · 2019
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Improved few-shot visual classification
Bateni, P., Goyal, R., Masrani, V., Wood, F., and Sigal, L. (2020) · 2020
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Modular meta-learning with shrinkage
Chen, Y., Friesen, A. L., Behbahani, F., Doucet, A., Budden, D., Hoffman, M., and de Freitas, N. (2020) · 2020
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Big transfer (bit): General visual representation learning
Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020) · 2020
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Bayesian meta-learning for the few-shot setting via deep kernels
Patacchiola, M., Turner, J., Crowley, E. J., O’Boyle, M., and Storkey, A. J. (2020) · 2020
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Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J. (2018) · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., Rodríguez López, P., and Lacoste, A. (2018) · 2018
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A. (2018) · 2018
Cited alongside, same era.
Efficient parametrization of multi-domain deep neural networks
Rebuffi, S.-A., Bilen, H., and Vedaldi, A. (2018) · 2018
Cited alongside, same era.
Convolutional networks with adaptive inference graphs
Veit, A. and Belongie, S. (2018) · 2018
Cited alongside, same era.
Blockdrop: Dynamic inference paths in residual networks
Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., and Feris, R. (2018) · 2018
Cited alongside, same era.
Spottune: transfer learning through adaptive fine-tuning
Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., and Feris, R. (2019) · 2019
Cited alongside, same era.
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Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., and Isola, P. (2020) · 2020
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Memory efficient meta-learning with large images
Bronskill, J., Massiceti, D., Patacchiola, M., Hofmann, K., Nowozin, S., and Turner, R. (2021) · 2021
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Comparing transfer and meta learning approaches on a unified few-shot classification benchmark
Dumoulin, V., Houlsby, N., Evci, U., Zhai, X., Goroshin, R., Gelly, S., and Larochelle, H. (2021) · 2021
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Dynamic neural networks: A survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., and Wang, Y. (2021) · 2021
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Orbit: A real-world few-shot dataset for teachable object recognition
Massiceti, D., Zintgraf, L., Bronskill, J., Theodorou, L., Harris, M. T., Cutrell, E., Morrison, C., Hofmann, K., and Stumpf, S. (2021) · 2021
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Non-gaussian gaussian processes for few-shot regression
Sendera, M., Tabor, J., Nowak, A., Bedychaj, A., Patacchiola, M., Trzcinski, T., Spurek, P., and Zieba, M. (2021) · 2021
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Meta-learning feature representations for adaptive gaussian processes via implicit differentiation
Chen, W., Tripp, A., and Hernández-Lobato, J. M. (2022) · 2022
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Cross-domain few-shot learning with task-specific adapters
Li, W.-H., Liu, X., and Bilen, H. (2022) · 2022
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