Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
<|reference_start|>Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration: Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input characteristics. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data.<|reference_end|>
@article{cheng2024instance-level,
    title={Instance-Level Safety-Aware Fidelity of Synthetic Data and Its
  Calibration},
    author={Chih-Hong Cheng, Paul St"ockel, Xingyu Zhao},
    journal={arXiv preprint arXiv:2402.07031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07031},
    primaryClass={cs.SE cs.AI cs.LG}
}