Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion
<|reference_start|>Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion: Synthetic data generation is an important application of machine learning in the field of medical imaging. While existing approaches have successfully applied fine-tuned diffusion models for synthesizing medical images, we explore potential improvements to this pipeline through feature-aligned diffusion. Our approach aligns intermediate features of the diffusion model to the output features of an expert, and our preliminary findings show an improvement of 9% in generation accuracy and ~0.12 in SSIM diversity. Our approach is also synergistic with existing methods, and easily integrated into diffusion training pipelines for improvements. We make our code available at \url{https://github.com/lnairGT/Feature-Aligned-Diffusion}.<|reference_end|>
@article{nair2024improved,
    title={Improved Generation of Synthetic Imaging Data Using Feature-Aligned
  Diffusion},
    author={Lakshmi Nair},
    journal={arXiv preprint arXiv:2410.00731},
    year={2024},
    doi={10.1145/3689096.3689460},
    archivePrefix={arXiv},
    eprint={2410.00731},
    primaryClass={cs.CV cs.LG}
}