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Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J · 1989
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Learning complex, extended sequences using the principle of history compression
Schmidhuber, J · 1992
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Catastrophic interference is eliminated in pretrained networks
McRae, K. and Hetherington, P. A · 1993
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Parameterisation of a stochastic model for human face identification
Samaria, F. S. and Harter, A. C · 1994
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Columbia object image library (coil-20)
Nene, S. A., Nayar, S. K., Murase, H., et al · 1996
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Catastrophic forgetting in connectionist networks
French, R. M · 1999
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Slow feature analysis: Unsupervised learning of invariances
Wiskott, L. and Sejnowski, T. J · 2002
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Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., and Bottou, L · 2004
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The template update problem
Matthews, I., Ishikawa, T., and Baker, S · 2004
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Unsupervised learning of visual invariance with temporal coherence
Zou, W. Y., Ng, A. Y., and Yu, K · 2004
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Semi-supervised self-training of object detection models
Rosenberg, C., Hebert, M., and Schneiderman, M · 2005
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Semi-supervised learning literature survey
Zhu, X · 2005
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Semi-supervised learning
Chapelle, O., Schölkopf, B., Zien, A., et al · 2006
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Ranzato, M. A., Huang, F. J., Boureau, Y., and LeCun, Y · 2007
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Unsupervised natural experience rapidly alters invariant object representation in visual cortex
Li, N. and DiCarlo, J. J · 2008
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Biometric template update: an experimental investigation on the relationship between update errors and performance degradation in face verification
Marcialis, G. L., Rattani, A., and Roli, F · 2008
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Towards a mathematical theory of cortical micro-circuits
George, D. and Hawkins, J · 2009
Tiled convolutional neural networks
Ngiam, J., Chen, Z., Chia, D., Koh, P. W., Le, Q. V., and Ng, A. Y · 2010
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Pattern recognition by hierarchical temporal memory
Maltoni, D · 2011
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On random weights and unsupervised feature learning
Saxe, A., Koh, P. W., Chen, Z., Bhand, M., Suresh, B., and Ng, A. Y · 2011
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Deep learning via semi-supervised embedding
Weston, J., Ratle, F., Mobahi, H., and Collobert, R · 2012
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Deep learning of invariant features via simulated fixations in video
Zou, W., Zhu, S., Yu, K., and Ng, A. Y · 2012
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The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects
Mermillod, M., Bugaiska, A., and Bonin, P · 2013
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Deep learning from temporal coherence in video
Mobahi, H., Collobert, R., and Weston, J · 2009
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Template update methods in adaptive biometric systems: a critical review
Rattani, A., Freni, B., Marcialis, G. L., and Roli, F · 2009
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Ordinal measures for iris recognition
Sun, Z. and Tan, T · 2009
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Theano: a cpu and gpu math expression compiler
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y · 2010
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Incremental template updating for face recognition in home environments
Franco, A., Maio, D., and Maltoni, D · 2010
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Learning convolutional neural networks from few samples
Wagner, R., Thom, M., Schweiger, R., Palm, G., and Rothermel, A · 2013
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Incremental learning by message passing in hierarchical temporal memory
Rehn, E. M. and Maltoni, D · 2014
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An empirical investigation of catastrophic forgeting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2015
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Unsupervised feature learning from temporal data
Goroshin, R., Bruna, J., Tompson, J., Eigen, D., and LeCun, Y · 2015
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