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Given new tasks with very little data$-$such as new classes in a classification problem or a domain shift in the input$-$performance of modern vision systems degrades remarkably quickly.
Minimization methods for nonsmooth convex and quasiconvex functions
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
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Learning from one example through shared densities on transforms
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S. Hochreiter, A. S. Younger, and P. R. Conwell · 2001
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Shape matching: Similarity measures and algorithms
R. C. Veltkamp · 2001
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Meta-learning with backpropagation
A. S. Younger, S. Hochreiter, and P. R. Conwell · 2001
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A perspective view and survey of meta-learning
R. Vilalta and Y. Drissi · 2002
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Video Google: A text retrieval approach to object matching in videos
J. Sivic and A. Zisserman · 2003
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Cross-generalization: Learning novel classes from a single example by feature replacement
E. Bart and S. Ullman · 2005
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Finite-time convergent gradient flows with applications to network consensus
J. Cortés · 2006
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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Discriminative object class models of appearance and shape by correlatons
S. Savarese, J. Winn, and A. Criminisi · 2006
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Local features and kernels for classification of texture and object categories: A comprehensive study
J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid · 2007
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Poselets: Body part detectors trained using 3d human pose annotations
L. Bourdev and J. Malik · 2009
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Partial similarity of objects, or how to compare a centaur to a horse
A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, and R. Kimmel · 2009
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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2009
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The Caltech-UCSD Birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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What makes paris look like paris?
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. A. Efros · 2012
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Fast image scanning with deep max-pooling convolutional neural networks
A. Giusti, D. C. Cireşan, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2013
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Blocks that shout: Distinctive parts for scene classification
M. Juneja, A. Vedaldi, C. Jawahar, and A. Zisserman · 2013
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Probabilistic elastic matching for pose variant face verification
H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi · 2013
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Efficient on-the-fly category retrieval using convnets and gpus
K. Chatfield, K. Simonyan, and A. Zisserman · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg · 2015
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Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
Few-shot learning with graph neural networks
V. Garcia and J. Bruna · 2018
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Unsupervised representation learning by predicting image rotations
S. Gidaris, P. Singh, and N. Komodakis · 2018
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On first-order meta-learning algorithms
J. Nichol, Alex any Andrychowicz ed Achiam and J. Schulman · 2018
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Film: Visual reasoning with a general conditioning layer
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville · 2018
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Neighbourhood consensus networks
I. Rocco, M. Cimpoi, R. Arandjelović, A. Torii, T. Pajdla, and J. Sivic · 2018
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Memory-based parameter adaptation
P. Sprechmann, S. M. Jayakumar, J. W. Rae, A. Pritzel, A. P. Badia, B. Uria, O. Vinyals, D. Hassabis, R. Pascanu, and C. Blundell · 2018
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Gradient-based hyperparameter optimization through reversible learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
Cited alongside, same era.
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, et al · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas · 2016
Cited alongside, same era.
Learning feed-forward one-shot learners
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi · 2016
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D. Ha, A. Dai, and Q. V. Le · 2016
Cited alongside, same era.
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
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Tracking emerges by colorizing videos
C. Vondrick, A. Shrivastava, A. Fathi, S. Guadarrama, and K. Murphy · 2018
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Non-local neural networks
X. Wang, R. Girshick, A. Gupta, and K. He · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
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Comparator networks
W. Xie, L. Shen, and A. Zisserman · 2018
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Learning representations by maximizing mutual information across views
P. Bachman, R. D. Hjelm, and W. Buchwalter · 2019
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Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. H. Torr, and A. Vedaldi · 2019
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Autoaugment: Learning augmentation strategies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2019
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Boosting few-shot visual learning with self-supervision
S. Gidaris, A. Bursuc, N. Komodakis, P. Perez, and M. Cord · 2019
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Revisiting self-supervised visual representation learning
A. Kolesnikov, X. Zhai, and L. Beyer · 2019
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Dense classification and implanting for few-shot learning
Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc · 2019
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Selflow: Self-supervised learning of optical flow
P. Liu, M. Lyu, I. King, and J. Xu · 2019
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Fast and flexible multi-task classification using conditional neural adaptive processes
J. Requeima, J. Gordon, J. Bronskill, S. Nowozin, and R. E. Turner · 2019
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Y. Tian, D. Krishnan, and P. Isola · 2019
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Learning compositional representations for few-shot recognition
P. Tokmakov, Y.-X. Wang, and M. Hebert · 2019
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Self-attention generative adversarial networks
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena · 2019
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Unifying deep local and global features for image search
B. Cao, A. Araujo, and J. Sim · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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A new meta-baseline for few-shot learning
Y. Chen, X. Wang, Z. Liu, H. Xu, and T. Darrell · 2020
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A baseline for few-shot image classification
G. S. Dhillon et al · 2020
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Momentum contrast for unsupervised visual representation learning
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. v. d. Maaten · 2020
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Optimized generic feature learning for few-shot classification across domains
T. Saikia, T. Brox, and C. Schmid · 2020
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When does self-supervision improve few-shot learning?
J.-C. Su, S. Maji, and B. Hariharan · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola · 2020
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Learning and aggregating deep local descriptors for instance-level recognition
G. Tolias, T. Jenicek, and O. Chum · 2020
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Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. J. Swersky, P.-A. Manzagol, and H. Larochelle · 2020
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
K. Yang, K. Qinami, L. Fei-Fei, J. Deng, and O. Russakovsky · 2020
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