# Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

Source: [Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa66651e209131cea10139>)

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

Data updated: 2025-10-25T21:49:28.917Z

Authors: Samarth Shukla, Luc Van Gool, Radu Timofte

Published: 2019-09-18T16:09:41.000Z

## Abstract

Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature. The current I2I translation approaches require training images from the two domains that are either all paired (supervised) or all unpaired (unsupervised). In practice, obtaining paired training data in sufficient quantities is often very costly and cumbersome. Therefore solutions that employ unpaired data, while less accurate, are largely preferred. In this paper, we aim to bridge the gap between supervised and unsupervised I2I translation, with application to semantic image segmentation. We build upon pix2pix and CycleGAN, state-of-the-art seminal I2I translation techniques. We propose a method to select (very few) paired training samples and achieve significant improvements in both supervised and unsupervised I2I translation settings over random selection. Further, we boost the performance by incorporating both (selected) paired and unpaired samples in the training process. Our experiments show that an extremely weak supervised I2I translation solution using only one paired training sample can achieve a quantitative performance much better than the unsupervised CycleGAN model, and comparable to that of the supervised pix2pix model trained on thousands of pairs.

## 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/1909.08542>)

[PDF](<https://arxiv.org/pdf/1909.08542.pdf>)