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Meta-learning has been proposed as a framework to address the challenging few-shot learning setting.
Using fast weights to deblur old memories
H. E. Geoffrey and P. C. David · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. J. Cohen · 1989
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On the optimization of a synaptic learning rule
S. Bengio, Y. Bengio, J. Cloutier, and J. Gecsei · 1992
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Meta-neural networks that learn by learning
D. K. Naik and R. Mammone · 1992
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Learning to learn: Introduction and overview
S. Thrun and L. Pratt · 1998
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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One-shot learning of object categories
F. Li, R. Fergus, and P. Perona · 2006
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Adapting SVM classifiers to data with shifted distributions
J. Yang, R. Yan, and A. G. Hauptmann · 2007
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Why does unsupervised pre-training help deep learning?
D. Erhan, Y. Bengio, A. C. Courville, P. Manzagol, P. Vincent, and S. Bengio · 2010
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Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2011
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, and C. H. Lampert · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Deep learning
L. Yann, B. Yoshua, and H. Geoffrey · 2015
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Large scale hard sample mining with monte carlo tree search
O. Canévet and F. Fleuret · 2016
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Fast and accurate deep network learning by exponential linear units (elus)
D. Clevert, T. Unterthiner, and S. Hochreiter · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. P. Lillicrap · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. B. Girshick · 2016
Cited alongside, same era.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Automated curriculum learning for neural networks
A. Graves, M. G. Bellemare, J. Menick, R. Munos, and K. Kavukcuoglu · 2017
Cited alongside, same era.
Smart mining for deep metric learning
B. Harwood, V. Kumar, G. Carneiro, I. Reid, and T. Drummond · 2017
Cited alongside, same era.
Bilevel programming for hyperparameter optimization and meta-learning
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil · 2018
Closest in time.
Recasting gradient-based meta-learning as hierarchical bayes
E. Grant, C. Finn, S. Levine, T. Darrell, and T. L. Griffiths · 2018
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Learning structure and strength of CNN filters for small sample size training
R. Keshari, M. Vatsa, R. Singh, and A. Noore · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Y. Lee and S. Choi · 2018
Closest in time.
Meta-sgd: Learning to learn quickly for few shot learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2018
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Snail: A simple neural attentive meta-learner
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel · 2018
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2017
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and K. Murphy · 2017
Cited alongside, same era.
Lucid data dreaming for object tracking
A. Khoreva, R. Benenson, E. Ilg, T. Brox, and B. Schiele · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
D. Lopez-Paz and M. Ranzato · 2017
Cited alongside, same era.
Generative adversarial residual pairwise networks for one shot learning
A. Mehrotra and A. Dukkipati · 2017
Cited alongside, same era.
Meta networks
T. Munkhdalai and H. Yu · 2017
Cited alongside, same era.
Rapid adaptation with conditionally shifted neurons
T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler · 2018
Closest in time.
TADAM: task dependent adaptive metric for improved few-shot learning
B. N. Oreshkin, P. Rodríguez, and A. Lacoste · 2018
Closest in time.
Film: Visual reasoning with a general conditioning layer
E. Perez, F. Strub, H. de Vries, V. Dumoulin, and A. C. Courville · 2018
Closest in time.
Few-shot image recognition by predicting parameters from activations
S. Qiao, C. Liu, W. Shen, and A. L. Yuille · 2018
Closest in time.
Delta-encoder: an effective sample synthesis method for few-shot object recognition
E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, R. S. Feris, A. Kumar, R. Giryes, and A. M. Bronstein · 2018
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Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
T. R. Scott, K. Ridgeway, and M. C. Mozer · 2018
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Natural and effective obfuscation by head inpainting
Q. Sun, L. Ma, S. Joon Oh, L. Van Gool, B. Schiele, and M. Fritz · 2018
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales · 2018
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Low-shot learning from imaginary data
Y. Wang, R. B. Girshick, M. Hebert, and B. Hariharan · 2018
Closest in time.
Transfer learning via learning to transfer
Y. Wei, Y. Zhang, J. Huang, and Q. Yang · 2018
Closest in time.
Curriculum learning by transfer learning: Theory and experiments with deep networks
D. Weinshall, G. Cohen, and D. Amir · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. B. Shen, L. J. Guibas, J. Malik, and S. Savarese · 2018
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Metagan: An adversarial approach to few-shot learning
R. Zhang, T. Che, Z. Grahahramani, Y. Bengio, and Y. Song · 2018
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Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2019
Closest in time.