Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

📰 ArXiv cs.AI

Learning from synthetic data using provenance-based input gradient guidance improves model robustness

advanced Published 6 Apr 2026

Action Steps

Generate synthetic data to augment existing training datasets
Use provenance-based input gradient guidance to identify informative regions in the input space
Train models using the guided synthetic data to improve discrimination and robustness
Evaluate and refine the model using metrics such as accuracy and robustness

Who Needs to Know This

Machine learning researchers and engineers benefit from this approach as it enhances model discrimination and robustness, while data scientists can apply these techniques to improve model performance

Key Insight

💡 Provenance-based input gradient guidance can explicitly teach models which input regions contribute to discrimination

Key Takeaways

Learning from synthetic data using provenance-based input gradient guidance improves model robustness

Full Article

Title: Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

Abstract:
arXiv:2604.02946v1 Announce Type: cross Abstract: Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby improving the robustness of model discrimination. However, many existing methods improve robustness only indirectly through the diversification of training samples and do not explicitly teach the model which regions in the input space truly contribute to discrimination; cons

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