# Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time

Source: [Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time](<https://www.sota2.com/research/paper/68fa66761e209131cea10492>)

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

Data updated: 2025-10-25T21:50:28.993Z

Authors: Assia Benbihi, Matthieu Geist, C\\'edric Pradalier

Published: 2018-05-10T19:14:06.000Z

## Abstract

Deep learning generates state-of-the-art semantic segmentation provided that a large number of images together with pixel-wise annotations are available. To alleviate the expensive data collection process, we propose a semi-supervised domain adaptation method for the specific case of images with similar semantic content but different pixel distributions. A network trained with supervision on a past dataset is finetuned on the new dataset to conserve its features maps. The domain adaptation becomes a simple regression between feature maps and does not require annotations on the new dataset. This method reaches performances similar to classic transfer learning on the PASCAL VOC dataset with synthetic transformations.

## 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/1805.04141>)

[PDF](<https://arxiv.org/pdf/1805.04141.pdf>)