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Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Triantafillou, E.; Zhu, T.; Dumoulin, V.; Lamblin, P.; Xu, K.; Goroshin, R.; Gelada, C.; Swersky, K.; Manzagol, P.; and Larochelle, H. 2019 · 1903
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Kruspe, A. 2019 · 1906
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Rapid learning or feature reuse? towards understanding the effectiveness of maml
Raghu, A.; Raghu, M.; Bengio, S.; and Vinyals, O. 2019 · 1909
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Meta-learnt priors slow down catastrophic forgetting in neural networks
Spigler, G. 2019 · 1909
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Regularizing Deep Multi-Task Networks using Orthogonal Gradients
Suteu, M.; and Guo, Y. 2019 · 1912
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J. 1987 · 1987
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One-class classifier networks for target recognition applications
Moya, M. M.; Koch, M. W.; and Hostetler, L. D. 1993 · 1993
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Multitask learning
Caruana, R. 1997 · 1997
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Support vector method for novelty detection
Schölkopf, B.; Williamson, R. C.; Smola, A. J.; Shawe-Taylor, J.; and Platt, J. C. 2000 · 2000
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SciPy: Open source scientific tools for Python
Jones, E.; Oliphant, T.; Peterson, P.; et al. 2001– · 2001
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Estimating the support of a high-dimensional distribution
Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; and Williamson, R. C. 2001 · 2001
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Outlier detection using replicator neural networks
Hawkins, S.; He, H.; Williams, G.; and Baxter, R. 2002 · 2002
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TaskNorm: Rethinking Batch Normalization for Meta-Learning
Bronskill, J.; Gordon, J.; Requeima, J.; Nowozin, S.; and Turner, R. E. 2020 · 2003
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A brain tumor segmentation framework based on outlier detection
Prastawa, M.; Bullitt, E.; Ho, S.; and Gerig, G. 2004 · 2004
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Support vector data description
Tax, D. M.; and Duin, R. P. 2004 · 2004
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Reducing the dimensionality of data with neural networks
Hinton, G. E.; and Salakhutdinov, R. R. 2006 · 2006
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Isolation forest
Liu, F. T.; Ting, K. M.; and Zhou, Z.-H. 2008 · 2008
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Anomaly detection: A survey
Chandola, V.; Banerjee, A.; and Kumar, V. 2009 · 2009
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Anomaly-based network intrusion detection: Techniques, systems and challenges
Garcia-Teodoro, P.; Diaz-Verdejo, J.; Maciá-Fernández, G.; and Vázquez, E. 2009 · 2009
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The MNIST database of handwritten digits
LeCun, Y.; Cortes, C.; and Burges, C. J. 2010 · 2010
Cited alongside, same era.
One shot learning of simple visual concepts
Lake, B.; Salakhutdinov, R.; Gross, J.; and Tenenbaum, J. 2011 · 2011
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Cited alongside, same era.
One-class classification: taxonomy of study and review of techniques
Khan, S. S.; and Madden, M. G. 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Cited alongside, same era.
Outlier analysis
Aggarwal, C. C. 2015 · 2015
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
Finn, C.; and Levine, S. 2017 · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z.; Zhou, F.; Chen, F.; and Li, H. 2017 · 2017
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Abnormal event detection in videos using generative adversarial nets
Ravanbakhsh, M.; Nabi, M.; Sangineto, E.; Marcenaro, L.; Regazzoni, C.; and Sebe, N. 2017 · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T.; Seeböck, P.; Waldstein, S. M.; Schmidt-Erfurth, U.; and Langs, G. 2017 · 2017
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Variational autoencoder based anomaly detection using reconstruction probability
An, J.; and Cho, S. 2015 · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Cited alongside, same era.
Siamese Neural Networks for One-Shot Image Recognition
Koch, G. R. 2015 · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Lake, B. M.; Salakhutdinov, R.; and Tenenbaum, J. B. 2015 · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
Cited alongside, same era.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Xu, D.; Ricci, E.; Yan, Y.; Song, J.; and Sebe, N. 2015 · 2015
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
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Meta-learning with differentiable closed-form solvers
Bertinetto, L.; Henriques, J. F.; Torr, P. H.; and Vedaldi, A. 2018 · 2018
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Unsupervised Learning via Meta-Learning
Hsu, K.; Levine, S.; and Finn, C. 2018 · 2018
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CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition
Kozerawski, J.; and Turk, M. 2018 · 2018
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Gradient-based meta-learning with learned layerwise metric and subspace
Lee, Y.; and Choi, S. 2018 · 2018
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Reptile: a Scalable Metalearning Algorithm
Nichol, A.; and Schulman, J. 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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Deep one-class classification
Ruff, L.; Vandermeulen, R.; Goernitz, N.; Deecke, L.; Siddiqui, S. A.; Binder, A.; Müller, E.; and Kloft, M. 2018 · 2018
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Meta-Learning with Latent Embedding Optimization
Rusu, A. A.; Rao, D.; Sygnowski, J.; Vinyals, O.; Pascanu, R.; Osindero, S.; and Hadsell, R. 2018 · 2018
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Adversarially learned one-class classifier for novelty detection
Sabokrou, M.; Khalooei, M.; Fathy, M.; and Adeli, E. 2018 · 2018
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Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm
Scime, L.; and Beuth, J. 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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Meta-learning with differentiable convex optimization
Lee, K.; Maji, S.; Ravichandran, A.; and Soatto, S. 2019 · 2019
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