Inversion Circle Interpolation: A Diffusion-Based Image Augmentation Method for Improving Image Classification in Data-Scarce Scenarios

Diffusion-based image augmentation methods often struggle to balance faithfulness (preserving original image characteristics) and diversity (creating varied synthetic images), limiting their effectiveness in data-scarce scenarios. This paper introduces Diff-II, a novel method using inversion circle interpolation and two-stage denoising to generate both faithful and diverse augmented images, improving classification performance across various tasks.