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The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels.
A new meta-baseline for few-shot learning
Chen, Y.; Wang, X.; Liu, Z.; Xu, H.; and Darrell, T. 2020 · 2003
Earlier work this paper cites.
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 · 2003
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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The caltech-ucsd birds-200-2011 dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Learning to learn
Thrun, S.; and Pratt, L. 2012 · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Going deeper with convolutions
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015 · 2015
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Learning to learn by gradient descent by gradient descent
Andrychowicz, M.; Denil, M.; Gomez, S.; Hoffman, M. W.; Pfau, D.; Schaul, T.; Shillingford, B.; and De Freitas, N. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Optimization as a model for few-shot learning
Ravi, S.; and Larochelle, H. 2016 · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 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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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
Cited alongside, same era.
Few-shot learning with graph neural networks
Garcia, V.; and Bruna, J. 2017 · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G.; Liu, Z.; Van Der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z.; Zhou, F.; Chen, F.; and Li, H. 2017 · 2017
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Liu, H.; Simonyan, K.; Vinyals, O.; Fernando, C.; and Kavukcuoglu, K. 2017 · 2017
Cited alongside, same era.
Few-shot learning with metric-agnostic conditional embeddings
Hilliard, N.; Phillips, L.; Howland, S.; Yankov, A.; Corley, C. D.; and Hodas, N. O. 2018 · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B.; López, P. R.; and Lacoste, A. 2018 · 2018
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Low-shot learning with imprinted weights
Qi, H.; Brown, M.; and Lowe, D. G. 2018 · 2018
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Few-shot image recognition by predicting parameters from activations
Qiao, S.; Liu, C.; Shen, W.; and Yuille, A. L. 2018 · 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 · 2018
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Group normalization
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Mishra, N.; Rohaninejad, M.; Chen, X.; and Abbeel, P. 2017 · 2017
Cited alongside, same era.
Meta networks
Munkhdalai, T.; and Yu, H. 2017 · 2017
Cited alongside, same era.
Rapid adaptation with conditionally shifted neurons
Munkhdalai, T.; Yuan, X.; Mehri, S.; and Trischler, A. 2017 · 2017
Cited alongside, same era.
Large-scale evolution of image classifiers
Real, E.; Moore, S.; Selle, A.; Saxena, S.; Suematsu, Y. L.; Tan, J.; Le, Q. V.; and Kurakin, A. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
Cited alongside, same era.
Genetic cnn
Xie, L.; and Yuille, A. 2017 · 2017
Cited alongside, same era.
Antoniou, A.; Edwards, H.; and Storkey, A. 2018 · 2018
Cited alongside, same era.
Wu, Y.; and He, K. 2018 · 2018
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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 · 2019
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Meta-learning with differentiable convex optimization
Lee, K.; Maji, S.; Ravichandran, A.; and Soatto, S. 2019 · 2019
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Evolving deep neural networks
Miikkulainen, R.; Liang, J.; Meyerson, E.; Rawal, A.; Fink, D.; Francon, O.; Raju, B.; Shahrzad, H.; Navruzyan, A.; Duffy, N.; et al. 2019 · 2019
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Regularized evolution for image classifier architecture search
Real, E.; Aggarwal, A.; Huang, Y.; and Le, Q. V. 2019 · 2019
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Meta-Learning of Neural Architectures for Few-Shot Learning
Elsken, T.; Staffler, B.; Metzen, J. H.; and Hutter, F. 2020 · 2020
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Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning
Kim, J.; Kim, H.; and Kim, G. 2020 · 2020
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Few-shot learning via embedding adaptation with set-to-set functions
Ye, H.-J.; Hu, H.; Zhan, D.-C.; and Sha, F. 2020 · 2020
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DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover’s Distance and Structured Classifiers
Zhang, C.; Cai, Y.; Lin, G.; and Shen, C. 2020 · 2020
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