Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

1. 1Generate synthetic data to augment existing training datasets
2. 2Use provenance-based input gradient guidance to identify informative regions in the input space
3. 3Train models using the guided synthetic data to improve discrimination and robustness
4. 4Evaluate and refine the model using metrics such as accuracy and robustness

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

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

🤖 Improve model robustness with synthetic data & provenance-based guidance!