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Slow feature analysis (SFA) is an unsupervised-learning algorithm that extracts slowly varying features from a multi-dimensional time series.
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G. Guo, G. Mu, Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression, in: CVPR, 2011, pp. 657–664
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2009
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A. N. Escalante-B., L. Wiskott, Gender and age estimation from synthetic face images with hierarchical slow feature analysis., in: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2010, pp. 240–249
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V. R. Kompella, M. D. Luciw, J. Schmidhuber, Incremental slow feature analysis: Adaptive low-complexity slow feature updating from high-dimensional input streams, Neural Computation 24 (11) (2012) 2994–3024
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A. N. Escalante-B., L. Wiskott, Slow feature analysis: Perspectives for technical applications of a versatile learning algorithm, Künstliche Intelligenz [Artificial Intelligence] 26 (4) (2012) 341–348
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A. N. Escalante-B., L. Wiskott, How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis, Journal of Machine Learning Research 14 (2013) 3683–3719
2013
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G. Guo, G. Mu, A framework for joint estimation of age, gender and ethnicity on a large database, Image and Vision Computing 32 (10) (2014) 761–770, best of Automatic Face and Gesture Recognition 2013
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Advances in Neural Information Processing Systems 27, Curran Associates, Inc., 2014, pp. 2672–2680
2014
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2015
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D. Yi, Z. Lei, S. Li, Age estimation by multi-scale convolutional network, in: Computer Vision – ACCV 2014, Vol. 9005 of Lecture Notes in Computer Science, Springer International Publishing, 2015, pp. 144–158
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E. L. Denton, S. Chintala, a. szlam, R. Fergus, Deep generative image models using a laplacian pyramid of adversarial networks, in: Advances in Neural Information Processing Systems 28, Curran Associates, Inc., 2015, pp. 1486–1494
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