A hybrid diffusion/inpainting pipeline grows a 656-image dermatology set into 266k synthetic images, lifting DDI malignancy classification to 90.9% and improving skin-tone fairness metrics.
An Improved Method for Personalizing Diffusion Models
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abstract
Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific objects based on diverse textual contexts. Our proposed approach aims to retain the model's original knowledge during new information integration, resulting in superior outcomes while necessitating less training time compared to Dreambooth and textual inversion.
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cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
A hybrid diffusion/inpainting pipeline grows a 656-image dermatology set into 266k synthetic images, lifting DDI malignancy classification to 90.9% and improving skin-tone fairness metrics.