Name
Abstract | ||
|---|---|---|
We evaluate the ability of the popular Field-of-Experts (FoE) to model structure in images. As a test case we focus on modeling synthetic and natural textures. We find that even for modeling single textures, the FoE provides insufficient flexibility to learn good generative models - it does not perform any better than the much simpler Gaussian FoE. We propose an extended version of the FoE (allowing for bimodal potentials) and demonstrate that this novel formulation, when trained with a better approximation of the likelihood gradient, gives rise to a more powerful generative model of specific visual structure that produces significantly better results for the texture task. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.5244/C.23.115 | BMVC |
Keywords | Field | DocType |
power generation | Visual structure,Computer science,Gaussian,Artificial intelligence,Generative grammar,Machine learning,Generative model | Conference |
Citations | PageRank | References |
132 | 7.91 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Heess | 1 | 1762 | 94.77 |
| Christopher K. I. Williams | 2 | 6807 | 631.16 |
| geoffrey e hinton | 3 | 40435 | 4751.69 |