Deep neural networks thrive on abundant labeled data, yet obtaining large‑scale, high‑quality annotated datasets remains a bottleneck across many domains. Synthetic data generation offers a promising alternative, but existing tools often suffer from limited realism, rigid pipelines, or insufficient configurability.
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Ablation studies (see Appendix A) confirm that contribute the largest gain for the medical imaging task (+2.3 % AUC), whereas lighting variation is the dominant factor for object detection (+1.9 % mAP). Deep neural networks thrive on abundant labeled data,
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