Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

Researchers propose a new machine learning framework that uses provenance information from synthetic data generation to improve model training. The method uses input gradient guidance to suppress learning from non-target regions, reducing spurious correlations and improving discrimination accuracy across multiple AI tasks.