TripleD distills large image-restoration datasets into a 2% subset selected by entropy-based complexity scores and then fine-tuned with a CNN, reportedly preserving 90-95% of full-dataset performance.
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Distribution-aware Dataset Distillation for Efficient Image Restoration
TripleD distills large image-restoration datasets into a 2% subset selected by entropy-based complexity scores and then fine-tuned with a CNN, reportedly preserving 90-95% of full-dataset performance.