Natural image statistics neural representation learning


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.

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.