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Few-shot models have become a popular topic of research in the past years.
MNIST handwritten digit database
Y. LeCun, C. Cortes, and C. J. Burges · 1998
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Support vector method for novelty detection
B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt · 1999
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Support vector domain description
D. M. J. Tax and R. P. W. Duin · 1999
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Outlier detection using replicator neural networks
S. Hawkins, H. He, G. J. Williams, and R. A. Baxter · 2002
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Anomaly detection: A survey
V. Chandola, A. Banerjee, and V. Kumar · 2009
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Evaluation of the CNN design choices performance on ImageNet-2012
D. Mishkin · 2012
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One-class classification: Taxonomy of study and review of techniques
S. S. Khan and M. G. Madden · 2014
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
M. Sakurada and T. Yairi · 2014
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Deep learning for content-based image retrieval: A comprehensive study
J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li · 2014
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Event extraction via dynamic multi-pooling convolutional neural networks
Y. Chen, L. Xu, K. Liu, D. Zeng, and J. Zhao · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 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, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie · 2016
Cited alongside, same era.
Detecting anomalous data using auto-encoders
J. T. A. A. E. J. Morton and L. D. Griffin · 2016
Cited alongside, same era.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
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Attentive neural architecture for ad-hoc structured document retrieval
S. Balaneshinkordan, A. Kotov, and F. Nikolaev · 2018
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Retrieval of song lyrics from sung queries
A. M. Kruspe and M. Goto · 2018
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A review on deep learning techniques applied to answer selection
T. M. Lai, T. Bui, and S. Li · 2018
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Learning deep features for one-class classification
P. Perera and V. M. Patel · 2018
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Deep one-class classification
L. Ruff, N. Görnitz, L. Deecke, S. A. Siddiqui, R. A. Vandermeulen, A. Binder, E. Müller, and M. Kloft · 2018
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Cited alongside, same era.
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
S. Fort · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs · 2017
Cited alongside, same era.
Later among the works it cites.
Adversarially learned one-class classifier for novelty detection
M. Sabokrou, M. Khalooei, M. Fathy, and E. Adeli · 2018
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Deep learning for anomaly detection: A survey
R. Chalapathy and S. Chawla · 2019
Closest in time.
One-class convolutional neural network
P. Oza and V. M. Patel · 2019
Closest in time.