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

Source: [Mining self-similarity: Label super-resolution with epitomic representations](<https://www.sota2.com/research/paper/68fa6b6d1e209131cea2e46c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:49:06.844Z

Authors: Nikolay Malkin, Anthony Ortiz, Caleb Robinson, Nebojsa Jojic

Published: 2020-04-24T00:42:59.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.11498>)

[PDF](<https://arxiv.org/pdf/2004.11498.pdf>)