🤖 AI Summary
Existing synthetic data evaluation lacks unified, transferable quantitative metrics. This paper proposes a novel evaluation framework grounded in generalized cross-validation (GCV) and domain transfer learning. It constructs a cross-dataset performance matrix and defines two core metrics: *fidelity*, quantifying distributional similarity between synthetic and real data; and *generalization coverage*, measuring the task-transfer capability of synthetic data across diverse real-world source domains. The framework is model-agnostic and enables normalized, comparative evaluation of detectors such as YOLOv5s across heterogeneous datasets—including Virtual KITTI, KITTI, and BDD100K. Experiments demonstrate that the method effectively quantifies synthetic data quality, significantly enhancing evaluation generality, comparability, and utility for model optimization. It establishes a scalable, reproducible, and standardized evaluation paradigm for synthetic data development.
📝 Abstract
With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only drives innovations in data generation methods but also guides researchers in optimizing the utilization of these synthetic resources. However, current evaluation studies for synthetic datasets remain limited, lacking a universally accepted standard framework. To address this, this paper proposes a novel evaluation framework integrating generalized cross-validation experiments and domain transfer learning principles, enabling generalizable and comparable assessments of synthetic dataset quality. The framework involves training task-specific models (e.g., YOLOv5s) on both synthetic datasets and multiple real-world benchmarks (e.g., KITTI, BDD100K), forming a cross-performance matrix. Following normalization, a Generalized Cross-Validation (GCV) Matrix is constructed to quantify domain transferability. The framework introduces two key metrics. One measures the simulation quality by quantifying the similarity between synthetic data and real-world datasets, while another evaluates the transfer quality by assessing the diversity and coverage of synthetic data across various real-world scenarios. Experimental validation on Virtual KITTI demonstrates the effectiveness of our proposed framework and metrics in assessing synthetic data fidelity. This scalable and quantifiable evaluation solution overcomes traditional limitations, providing a principled approach to guide synthetic dataset optimization in artificial intelligence research.