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The paper proposes FDSML, a formula-driven supervised metric learning approach that uses parameter-aware triplet loss on a refined fractal dataset (FineFractalDB) to improve synthetic pre-training for image retrieval, achieving notable Recall@1 gains over FDSL on Stanford Online Products and Stanford Cars.