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arXiv · 2410.00731

Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion

Lakshmi Nair
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Abstract

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}.

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Lakshmi Nair. 2024-10-01. Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion. https://doi.org/10.1145/3689096.3689460

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