# Mining self-similarity: Label super-resolution with epitomic representations

arXiv: https://arxiv.org/abs/2004.11498 · HTML: https://mlsanity.com/paper/2004.11498/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [GFlowNet-EM for learning compositional latent variable models](https://mlsanity.com/paper/2302.06576/) (2023)
- [Resolving label uncertainty with implicit posterior models](https://mlsanity.com/paper/2202.14000/) (2022)
- [Handling Image and Label Resolution Mismatch in Remote Sensing](https://mlsanity.com/paper/2211.15790/) (2022)
- [High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest](https://mlsanity.com/paper/2101.01154/) (2021)
- [Interactive Visualization and Representation Analysis Applied to Glacier Segmentation](https://mlsanity.com/paper/2112.08184/) (2021)
- [Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery](https://mlsanity.com/paper/2401.06762/) (2024)

## Abstract (arXiv)

We show that simple patch-based models, such as epitomes, can have superior performance to the current state of the art in semantic segmentation and label super-resolution, which uses deep convolutional neural networks. We derive a new training algorithm for epitomes which allows, for the first time, learning from very large data sets and derive a label super-resolution algorithm as a statistical inference algorithm over epitomic representations. We illustrate our methods on land cover mapping and medical image analysis tasks.