# Probabilistic Semantic Inpainting with Pixel Constrained CNNs

arXiv: https://arxiv.org/abs/1810.03728 · HTML: https://mlsanity.com/paper/1810.03728/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion](https://mlsanity.com/paper/2305.03098/) (2023)
- [Deep autoregressive modeling for land use land cover](https://mlsanity.com/paper/2401.01395/) (2024)

## Abstract (arXiv)

Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a given missing region. In this paper, we propose a method to perform probabilistic semantic inpainting by building a model, based on PixelCNNs, that learns a distribution of images conditioned on a subset of visible pixels. Experiments on the MNIST and CelebA datasets show that our method produces diverse and realistic inpaintings.