# GeneSIS-RT: Generating Synthetic Images for training Secondary Real-world Tasks

arXiv: https://arxiv.org/abs/1710.04280 · HTML: https://mlsanity.com/paper/1710.04280/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation](https://mlsanity.com/paper/2203.09454/) (2022)
- [RARA: Zero-shot Sim2Real Visual Navigation with Following Foreground Cues](https://mlsanity.com/paper/2201.02798/) (2022)
- [Towards self-attention based visual navigation in the real world](https://mlsanity.com/paper/2209.07043/) (2022)
- [Bird's Eye View Based Pretrained World model for Visual Navigation](https://mlsanity.com/paper/2310.18847/) (2023)

## Abstract (arXiv)

We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent progress in image-to-image translation to bridge the gap between simulated and real images, allowing us to generate realistic training data for real-world tasks using only unlabeled real-world images and a simulation. GeneSIS-RT ameliorates the burden of having to collect labeled real-world images and is a promising candidate for generating high-quality, domain-specific, synthetic data. To show the effectiveness of using GeneSIS-RT to create training data, we study two tasks: semantic segmentation and reactive obstacle avoidance. We demonstrate that learning algorithms trained using data generated by GeneSIS-RT make high-accuracy predictions and outperform systems trained on raw simulated data alone, and as well or better than those trained on real data. Finally, we use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s through a cluttered environment, demonstrating that our GeneSIS-RT images can be used to learn to perform mission-critical tasks.