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A few-shot generative model should be able to generate data from a novel distribution by only observing a limited set of examples.
The mnist database of handwritten digits
LeCun, Y · 1998
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Metalearning
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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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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., and Wierstra, D · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Edwards, H. and Storkey, A · 2016
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Pixel recurrent neural networks
Oord, A. v. d., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2016
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One-shot generalization in deep generative models
Rezende, D. J., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D · 2016
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Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
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Matching networks for one shot learning
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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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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Few-shot autoregressive density estimation: Towards learning to learn distributions
Reed, S., Chen, Y., Paine, T., Oord, A. v. d., Eslami, S., Rezende, D., Vinyals, O., and de Freitas, N · 2017
Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M · 2018
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Probabilistic symmetry and invariant neural networks
Bloem-Reddy, B. and Teh, Y. W · 2019
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Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Few-shot generative modelling with generative matching networks
Bartunov, S. and Vetrov, D · 2018
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Neural scene representation and rendering
Eslami, S. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al · 2018
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Biva: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O · 2019
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Multi-digit mnist for few-shot learning, 2019
Sun, S.-H · 2019
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Metafun: Meta-learning with iterative functional updates
Xu, J., Ton, J.-F., Kim, H., Kosiorek, A. R., and Teh, Y. W · 2019
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Probabilistic symmetries and invariant neural networks
Bloem-Reddy, B. and Teh, Y. W · 2020
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Very deep vaes generalize autoregressive models and can outperform them on images
Child, R · 2020
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9. on the condition of partial exchangeability
De Finetti, B · 2020
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Meta-learning in neural networks: A survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2020
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Bayesian models of conceptual development: Learning as building models of the world
Ullman, T. D. and Tenenbaum, J. B · 2020
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NVAE: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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Meta-amortized variational inference and learning
Wu, M., Choi, K., Goodman, N. D., and Ermon, S · 2020
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Setvae: Learning hierarchical composition for generative modeling of set-structured data
Kim, J., Yoo, J., Lee, J., and Hong, S · 2021
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