# FQGA-single: Towards Fewer Training Epochs and Fewer Model Parameters for Image-to-Image Translation Tasks

arXiv: https://arxiv.org/abs/2408.09218 · HTML: https://mlsanity.com/paper/2408.09218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [One Epoch Is All You Need](https://mlsanity.com/paper/1906.06669/) (2019)

## Abstract (arXiv)

This paper proposes a novel model inspired by CycleGAN: FQGA-single to produce high quality medical synthetic CT (sCT) generated images more efficiently. Evaluations were done on the SynthRAD Grand Challenge dataset with the CycleGAN model used for benchmarking and for comparing the quality of CBCT-to-sCT generated images from both a quantitative and qualitative perspective. Finally, this paper also explores ideas from the paper "One Epoch Is All You Need" to compare models trained on a single epoch versus multiple epochs. Astonishing results from FQGA-single were obtained during this exploratory experiment, which show that the performance of FQGA-single when trained on a single epoch surpasses itself when trained on multiple epochs. More surprising is that its performance also surpasses CycleGAN's multiple-epoch and single-epoch models, and even a modified version of CycleGAN.