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  Generative Image Modeling Using Spatial LSTMs

Theis, L., & Bethge, M. (2016). Generative Image Modeling Using Spatial LSTMs. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28 (pp. 1918-1926). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7AB2-E Version Permalink: http://hdl.handle.net/21.11116/0000-0000-7AB3-D
Genre: Conference Paper

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 Creators:
Theis, L1, 2, Author              
Bethge, M1, 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, ou_1497794              

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 Abstract: Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multi-dimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting.

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 Dates: 2016
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: TheisB2015
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Title: Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS 2015)
Place of Event: Montréal, Canada
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Title: Advances in Neural Information Processing Systems 28
Source Genre: Proceedings
 Creator(s):
Cortes, C., Editor
Lawrence, N.D., Editor
Lee, D.D., Editor
Sugiyama, M., Editor
Garnett, R., Editor
Garnett, R., Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1918 - 1926 Identifier: -