# Generative Image Modeling

We use generative image models to study the probability distribution of natural images. For a recent review on the topic, see Gerhard et al. (2015). We put particular emphasis on an information theoretic quantitative evaluation.*Redundancy reduction*(Barlow, 1959), for example, is one of several principles the brain might use to learn image representations in an unsupervised manner. Generative image models allow us to derive algorithms which perform redundancy reduction (e.g., Sinz & Bethge, 2008).

One question of interest to us is how to best exploit hierarchical representations for image modeling. Hierarchical generative image models were outperformed by much simpler models for a long time (Theis et al., 2011), but recent advances are starting to change that (e.g., Theis & Bethge, 2015).

## Selected References

L. Theis and M. Bethge**Generative Image Modeling Using Spatial LSTMs**

*Advances in Neural Information Processing Systems 28, 2015*

#deep learning, #generative modeling, #natural image statistics, #lstm, #mcgsm

Code, URL, PDF, Supplemental, BibTex

H. E. Gerhard, L. Theis, and M. Bethge

**Modeling Natural Image Statistics**

*Biologically-inspired Computer Visionâ€”Fundamentals and Applications (to appear), Wiley VCH, 2015, ISBN 978-3527412648*

#natural image statistics, #mcgsm, #ica, #psychophysics

URL, ISBN, PDF, BibTex

F. Sinz, J.-P. Lies, S. Gerwinn, and M. Bethge

**Natter: A Python Natural Image Statistics Toolbox**

*Journal of Statistical Software,*

**61**(5), 2014#natural image statistics, #software, #python

Code, PDF, BibTex

L. Theis, S. Gerwinn, F. Sinz, and M. Bethge

**In All Likelihood, Deep Belief Is Not Enough**

*Journal of Machine Learning Research,*

**12**, 3071-3096, 2011#natural image statistics, #deep belief networks, #boltzmann machines, #deep learning

Code, PDF, BibTex

R. Hosseini, F. Sinz, and M. Bethge

**Lower bounds on the redundancy of natural images**

*Vision Research,*

**50**(22), 2213-2222, 2010#natural image statistics

PDF, BibTex

F. Sinz and M. Bethge

**L**

_{p}-nested symmetric distributions*Journal of Machine Learning Research,*

**11**, 3409-3451, 2010#natural image statistics, #ica, #lp-spherically symmetric distributions, #nu-spherical symmetric distributions

PDF, BibTex

J. Eichhorn, F. Sinz, and M. Bethge

**Natural Image Coding in V1: How Much Use Is Orientation Selectivity?**

*PLoS Computational Biology,*

**5**(4), 2009#natural image models, #natural image statistics, #normative models

Code, DOI, PDF, BibTex

F. Sinz and M. Bethge

**The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction**

*Advances in Neural Information Processing Systems 21, 2008*

#contrast gain control, #normative models, #natural image statistics, #lp-spherically symmetric distributions

Code, PDF, BibTex

M. Bethge and R. Hosseini

**Method and Device for Image Compression**

*EuropĂ¤isches Patentamt, 2008*

#mixture, #image compression

URL, BibTex