Papers › Pixel Recurrent Neural Networks
Pixel Recurrent Neural Networks
Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
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.
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Code
Syntology Ran 19 of 29 code samples harvested from 9 repositories linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 17 ran with no contract checked.
By repository: community (archive-listed): 28 samples from 9 repositories, 18 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
20 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
29 samples harvested; 19 ran; 1 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 18 of the 29 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | Binarized MNIST | PixelRNN | nats | 79.20 | #5 of 10 | Archive leaderboard | report |
| Image Generation | Binarized MNIST | PixelCNN | nats | 81.30 | #6 of 10 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | PixelRNN | bpd | 3.86 | #28 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Masked Convolution, PixelCNN, PixelRNN
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