Erfan Esmaeili

Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while ove...