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  Natural image statistics neural representation learning

Bethge, M. (2014). Natural image statistics neural representation learning. Talk presented at Autonomous Learning: Summer School 2014. Leipzig, Germany.

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 Creators:
Bethge, Matthias1, 2, Author              
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: n important motivation for studying the statistics of natural images is the search for image representations which facilitate visual inference tasks. Representations optimized directly for a given task are at risk of overfitting, that is, the representations might work well for that particular task but might not generalize well to others. However, the striking ability of our visual system to perform well in a variety of different situations and to recognize objects even when they have been seen only once suggests that it exploits general structural regularities of natural images. In this lecture, I will give an overview on natural image statistics and how different types of representations have been derived by modeling different statistical properties of natural images.

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 Dates: 2014-09
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: Bethge2014_2
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Title: Autonomous Learning: Summer School 2014
Place of Event: Leipzig, Germany
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Invited: Yes

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