mlsanityData to 7 Oct 2026

Pixel Recurrent Neural Networks

arXiv 1601.06759 ↗First posted 25 Jan 2016cs.CV cs.LG cs.NEcited by 814

Not on the map. Why?

Cited by 142 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.

Key facts. Cited by 814 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2019: 1; 2020: 4; 2021: 156; 2022: 159; 2023: 140; 2024: 121; 2025: 141; 2026: 92.

814 papers.

Sort:

By area:

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  5. 2023Consistency Modelscited by 1,778
  6. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  7. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  8. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  9. 2021Variational Diffusion Modelscited by 1,145
  10. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  11. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,099
  12. 2022Building Normalizing Flows with Stochastic Interpolantscited by 964
38 more
  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  3. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  4. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
  5. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 670
  6. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
  7. 2024Autoregressive Image Generation without Vector Quantizationcited by 644
  8. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  9. 2024Simplified and Generalized Masked Diffusion for Discrete Datacited by 606
  10. 2022Fast Sampling of Diffusion Models with Exponential Integratorcited by 526
  11. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  12. 2023Aligning Text-to-Image Models using Human Feedbackcited by 418
  13. 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 376
  14. 2021GAN Inversion: A Surveycited by 366
  15. 2022MAGVIT: Masked Generative Video Transformercited by 341
  16. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  17. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 301
  18. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  19. 2021How to Train Your Energy-Based Modelscited by 271
  20. 2021Generating Images with Sparse Representationscited by 262
  21. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  22. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  23. 2023Using Human Feedback to Fine-tune Diffusion Models without Any Reward Modelcited by 246
  24. 2023Sequential Modeling Enables Scalable Learning for Large Vision Modelscited by 212
  25. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  26. 2022The Role of ImageNet Classes in Fr'echet Inception Distancecited by 211
  27. 2021Autoregressive Diffusion Modelscited by 208
  28. 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
  29. 2021PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformerscited by 204
  30. 2022Versatile Diffusion: Text, Images and Variations All in One Diffusion Modelcited by 199
  31. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  32. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  33. 2022Generative Modelling With Inverse Heat Dissipationcited by 164
  34. 2023MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformerscited by 164
  35. 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationcited by 162
  36. 2023MotionLM: Multi-Agent Motion Forecasting as Language Modelingcited by 154
  37. 2021StyleSwin: Transformer-based GAN for High-resolution Image Generationcited by 152
  38. 2024Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokenscited by 147

50 most cited of 814 All 814 (JSON)