mlsanityData to 7 Oct 2026

Generative Image Modeling Using Spatial LSTMs

arXiv 1506.03478 ↗First posted 10 Jun 2015stat.ML cs.CV cs.LGcited by 35

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Abstract (arXiv)

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.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2015.

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Citing papers per year: 2017: 2; 2018: 1; 2019: 0; 2020: 0; 2021: 6; 2022: 9; 2023: 4; 2024: 6; 2025: 5; 2026: 2.

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  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  2. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  3. 2022An Introduction to Neural Data Compressioncited by 78
  4. 2022Residual Mixture of Expertscited by 39
  5. 2025MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidancecited by 32
  6. 2022Regression as Classification: Influence of Task Formulation on Neural Network Featurescited by 29
  7. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  8. 2023Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Modelcited by 25
  9. 2021Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamicscited by 19
  10. 2018Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithmscited by 13
  11. 2025Mixture of Lookup Expertscited by 11
  12. 2021Lossless Coding of Point Cloud Geometry using a Deep Generative Modelcited by 10
23 more
  1. 2024Training-efficient density quantum machine learningcited by 10
  2. 2024Neural Experts: Mixture of Experts for Implicit Neural Representationscited by 9
  3. 2023SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecastingcited by 7
  4. 2024Designing Large Foundation Models for Efficient Training and Inference: A Surveycited by 7
  5. 2025MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Modelscited by 7
  6. 2022Denoising Deep Generative Modelscited by 6
  7. 2021Spatial Dependency Networks: Neural Layers for Improved Generative Image Modelingcited by 5
  8. 2024No Need to Talk: Asynchronous Mixture of Language Modelscited by 5
  9. 2022Short term prediction of demand for ride hailing services: A deep learning approachcited by 4
  10. 2025SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstructioncited by 4
  11. 2021CAGAN: Text-To-Image Generation with Combined Attention GANscited by 2
  12. 2022Neural Inverse Transform Samplercited by 2
  13. 2017An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modelingcited by 1
  14. 2021PixelPyramids: Exact Inference Models from Lossless Image Pyramidscited by 1
  15. 2023Compression of GPS Trajectories using Autoencoderscited by 1
  16. 2023Adversarial Likelihood Estimation With One-Way Flowscited by 1
  17. 2024Self-Supervised Learning Based Handwriting Verificationcited by 1
  18. 2025Poly-Autoregressive Prediction for Modeling Interactionscited by 1
  19. 2022LED: Latent Variable-based Estimation of Densitycited by 0
  20. 2022R-MelNet: Reduced Mel-Spectral Modeling for Neural TTScited by 0
  21. 2024HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecastingcited by 0
  22. 2026A Semiparametric Framework for Stochastic Fundamental Diagram Modelingcited by 0
  23. 2026Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modelingcited by 0