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Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.
On the optimization of a synaptic learning rule
Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J · 1992
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Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks
Schmidhuber, J · 1992
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Gaussian processes in machine learning
Rasmussen, C. E · 2003
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One shot learning of simple visual concepts
Lake, B., Salakhutdinov, R., Gross, J., and Tenenbaum, J · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Ha, D., Dai, A., and Le, Q. V · 2016
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Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
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Discriminative k-shot learning using probabilistic models, 2017
Bauer, M., Rojas-Carulla, M., Światkowski, J. B., Schölkopf, B., and Turner, R. E · 2017
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Emnist: an extension of mnist to handwritten letters (2017)
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Meta networks
Munkhdalai, T. and Yu, H · 2017
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Few-shot image recognition by predicting parameters from activations, 2017
Qiao, S., Liu, C., Shen, W., and Yuille, A · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
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Stochastic maximum likelihood optimization via hypernetworks
Sheikh, A.-S., Rasul, K., Merentitis, A., and Bergmann, U · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S · 2017
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P., and Vedaldi, A · 2018
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Probabilistic model-agnostic meta-learning
Finn, C., Xu, K., and Levine, S · 2018
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting
Gidaris, S. and Komodakis, N · 2018
Cited alongside, same era.
Meta-learning probabilistic inference for prediction
Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S · 2019
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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
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Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S. M., and Levine, S · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Wang, Y., Chao, W.-L., Weinberger, K. Q., and van der Maaten, L · 2019
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Meta-learning in neural networks: A survey, 2020
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., and Turner, R · 2018
Cited alongside, same era.
Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T · 2018
Cited alongside, same era.
A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2018
Cited alongside, same era.
Rapid adaptation with conditionally shifted neurons
Munkhdalai, T., Yuan, X., Mehri, S., and Trischler, A · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., Rodriguez, P., and Lacoste, A · 2018
Cited alongside, same era.
Amortized bayesian meta-learning
Ravi, S. and Beatson, A · 2018
Cited alongside, same era.
Uncertainty in model-agnostic meta-learning using variational inference
Nguyen, C., Do, T.-T., and Carneiro, G · 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
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Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes
Snell, J. and Zemel, R · 2020
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Generalizing from a few examples: A survey on few-shot learning, 2020
Wang, Y., Yao, Q., Kwok, J., and Ni, L. M · 2020
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Learning to learn variational semantic memory
Zhen, X., Du, Y.-J., Xiong, H., Qiu, Q., Snoek, C., and Shao, L · 2020
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A comprehensive survey on transfer learning, 2020
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q · 2020
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Leveraging the feature distribution in transfer-based few-shot learning, 2021
Hu, Y., Gripon, V., and Pateux, S · 2021
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Gaussian process meta few-shot classifier learning via linear discriminant laplace approximation
Kim, M. and Hospedales, T · 2021
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Amortized bayesian prototype meta-learning: A new probabilistic meta-learning approach to few-shot image classification
Sun, Z., Wu, J., Li, X., Yang, W., and Xue, J.-H · 2021
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Learning to learn dense gaussian processes for few-shot learning
Wang, Z., Miao, Z., Zhen, X., and Qiu, Q · 2021
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Hypertransformer: Model generation for supervised and semi-supervised few-shot learning, 2022
Zhmoginov, A., Sandler, M., and Vladymyrov, M · 2022
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