Slow feature analysis: Unsupervised learning of invariances, Part II

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Relevant Publications:

Black colored reference are the principal ones. Gray colored references are listed for the sake of completeness only. They contain little additional information. .ps-files are optimized for printing; .pdf-files are optimized for viewing at the computer.

5. Franzius, M., Wilbert, N., and Wiskott, L. (3. September 2008).
   Invariant Object Recognition with Slow Feature Analysis.
   Proc. 18th Int'l Conf. on Artificial Neural Networks, ICANN'08, Prague, September 3-6, eds. Vera Kurková, Roman Neruda, and Jan Koutník, publ. Springer-Verlag, pp. 961-970.
   (bibtex, abstract.html)
   
   
4. Franzius, M., Wilbert, N., and Wiskott, L. (12. July 2008).
   Unsupervised learning of invariant 3D-object and pose representations with slow feature analysis.
   Proc. Federation of European Neuroscience Societies (FENS) Forum 2008, Geneva, July 12-16 (abstract).
   (bibtex, abstract.html)
   
   
3. Wilbert, N., Franzius, M., Cichy, R., Schmidt, S., Brandt, S., and Wiskott, L. (3. December 2007).
   Towards a model of visual attention.
   Proc. Midterm Evaluation of the German National Network for Computational Neuroscience, Berlin, Germany, December 3--4, p. 30 (abstract).
   (bibtex, abstract.html)
   
   
2. Franzius, M., Wilbert, N., and Wiskott, L. (24. September 2007).
   Unsupervised learning of invariant 3D-object representations with slow feature analysis.
   Proc. 3rd Bernstein Symposium for Computational Neuroscience, Göttingen, September 24-27, p. 105 (abstract).
   (bibtex, abstract.html)
   
   
1. Wiskott, L., Franzius, M., Berkes, P., and Sprekeler, H. (15. September 2007).
   Is slowness a learning principle of the visual system?
   Proc. 39th Annual European Brain and Behaviour Society, EBBS, Triest, Italy, September 15-19, eds. Alessandro Treves et al., special issue of Neural Plasticity, Article ID 23250, pp. 14-15 (abstract).
   (bibtex, abstract.html)
   
   

Related Project:

• SFA: Unsupervised learning of invariances I (preceding project for learning invariances in 1D)