Timezone: »
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.
Author Information
Lucas Theis (U.Tuebingen)
Matthias Bethge (CIN, University Tübingen)
More from the Same Authors
-
2023 Poster: RDumb: A simple approach that questions our progress in continual test-time adaptation »
Ori Press · Steffen Schneider · Matthias Kümmerer · Matthias Bethge -
2023 Poster: Modulated Neural ODEs »
Ilze Amanda Auzina · Çağatay Yıldız · Sara Magliacane · Matthias Bethge · Efstratios Gavves -
2023 Poster: Compositional Generalization from First Principles »
Thaddäus Wiedemer · Prasanna Mayilvahanan · Matthias Bethge · Wieland Brendel -
2018 : Adversarial Vision Challenge: Results of the Adversarial Vision Challenge »
Wieland Brendel · Jonas Rauber · Marcel Salathé · Alexey Kurakin · Nicolas Papernot · Sharada Mohanty · Matthias Bethge -
2017 : DeepArt competition »
Alexander Ecker · Leon A Gatys · Matthias Bethge -
2017 Poster: Neural system identification for large populations separating “what” and “where” »
David Klindt · Alexander Ecker · Thomas Euler · Matthias Bethge -
2016 : Matthias Bethge - Texture perception in humans and machines »
Matthias Bethge -
2015 Poster: Texture Synthesis Using Convolutional Neural Networks »
Leon A Gatys · Alexander Ecker · Matthias Bethge -
2012 Poster: Training sparse natural image models with a fast Gibbs sampler of an extended state space »
Lucas Theis · Jascha Sohl-Dickstein · Matthias Bethge -
2010 Poster: Evaluating neuronal codes for inference using Fisher information »
Ralf Haefner · Matthias Bethge -
2009 Poster: Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions »
Fabian H Sinz · Eero Simoncelli · Matthias Bethge -
2009 Poster: Neurometric function analysis of population codes »
Philipp Berens · Sebastian Gerwinn · Alexander S Ecker · Matthias Bethge -
2009 Poster: A joint maximum-entropy model for binary neural population patterns and continuous signals »
Sebastian Gerwinn · Philipp Berens · Matthias Bethge -
2009 Spotlight: A joint maximum-entropy model for binary neural population patterns and continuous signals »
Sebastian Gerwinn · Philipp Berens · Matthias Bethge -
2009 Poster: Bayesian estimation of orientation preference maps »
Jakob H Macke · Sebastian Gerwinn · Leonard White · Matthias Kaschube · Matthias Bethge -
2008 Poster: The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction »
Fabian H Sinz · Matthias Bethge -
2008 Spotlight: The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction »
Fabian H Sinz · Matthias Bethge -
2007 Oral: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
Sebastian Gerwinn · Jakob H Macke · Matthias Seeger · Matthias Bethge -
2007 Spotlight: Near-Maximum Entropy Models for Binary Neural Representations of Natural Images »
Matthias Bethge · Philipp Berens -
2007 Poster: Near-Maximum Entropy Models for Binary Neural Representations of Natural Images »
Matthias Bethge · Philipp Berens -
2007 Poster: Bayesian Inference for Spiking Neuron Models with a Sparsity Prior »
Sebastian Gerwinn · Jakob H Macke · Matthias Seeger · Matthias Bethge -
2007 Poster: Receptive Fields without Spike-Triggering »
Jakob H Macke · Günther Zeck · Matthias Bethge