FQGA-single: Towards Fewer Training Epochs and Fewer Model Parameters for Image-to-Image Translation Tasks
<|reference_start|>FQGA-single: Towards Fewer Training Epochs and Fewer Model Parameters for Image-to-Image Translation Tasks: CycleGAN was trained on SynthRAD Grand Challenge Dataset using the single-epoch modification (SEM) method proposed in this paper which is referred to as (CycleGAN-single) compared to the usual method of training CycleGAN on around 200 epochs (CycleGAN-multi). Model performance were evaluated qualitatively and quantitatively with quantitative performance metrics like PSNR, SSIM, MAE and MSE. The consideration of both quantitative and qualitative performance when evaluating a model is unique to certain image-to-image translation tasks like medical imaging of patient data as detailed in this paper. Also, this paper shows that good quantitative performance does not always imply good qualitative performance and the converse is also not always True (i.e. good qualitative performance does not always imply good quantitative performance). This paper also proposes a lightweight model called FQGA (Fast Paired Image-to-Image Translation Quarter-Generator Adversary) which has 1/4 the number of parameters compared to CycleGAN (when comparing their Generator Models). FQGA outperforms CycleGAN qualitatively and quantitatively even only after training on 20 epochs. Finally, using SEM method on FQGA allowed it to again outperform CycleGAN both quantitatively and qualitatively. These performance gains even with fewer model parameters and fewer epochs (which will result in time and computational savings) may also be applicable to other image-to-image translation tasks in Machine Learning apart from the Medical image-translation task discussed in this paper between Cone Beam Computed Tomography (CBCT) and Computed Tomography (CT) images.<|reference_end|>
@article{yang2024fqga-single:,
    title={FQGA-single: Towards Fewer Training Epochs and Fewer Model Parameters
  for Image-to-Image Translation Tasks},
    author={Cho Yang},
    journal={arXiv preprint arXiv:2408.09218},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09218},
    primaryClass={eess.IV cs.CV cs.LG}
}