SDGym: A Benchmark for Evaluating Synthetic Data Generators, Valery Borisov, Anna P. J. G. de Vries, Michael R. King, and Tijl De Bie, 2022ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 16 (Association for Computing Machinery)DOI: 10.1145/3545122 - Introduces a systematic benchmark framework and evaluation methodology for comparing synthetic data generators on various tasks and metrics.
CTGAN: Effective and Efficient Table Data Synthesis, Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni, 2019Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vol. 32 (Neural Information Processing Systems Foundation, Inc.) - Presents a practical approach to generating and evaluating synthetic tabular data, including specific metrics and considerations for utility and fidelity assessments.