Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

We propose a training method that leverages provenance-based input gradient guidance to improve learning from synthetic data.

Citation: K. Nagano, R. Fujii, R. Hachiuma, F. Sato, T. Sekii, H. Saito, "Learning from Synthetic Data via Provenance-Based Input Gradient Guidance," CVPR, 2026.
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