Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
The framework proposes a novel prototype-guided diffusion model to distill large remote sensing datasets into compact, high-quality samples. It uses category prototypes as semantic anchors to guide the generation process, ensuring the resulting synthetic data maintains strong semantic fidelity and discriminative power for training other AI models.