Evaluating Synthetic Data Quality: Advanced Techniques
Chapter 1: Foundations of Synthetic Data Evaluation
Defining Data Quality Dimensions
Challenges in Evaluating Generated Data
The Fidelity-Utility-Privacy Trade-off
Taxonomy of Evaluation Metrics
Setting Up an Evaluation Environment
Chapter 2: Advanced Statistical Fidelity Assessment
Multivariate Distribution Comparisons
Hypothesis Testing for Distributional Similarity
Correlation and Covariance Structure Analysis
Information-Theoretic Measures
Propensity Score Evaluation
Hands-on practical: Implementing Multivariate Tests
Chapter 3: Evaluating Machine Learning Utility
Train-Synthetic-Test-Real (TSTR) Methodology
Train-Real-Test-Synthetic (TRTS) Methodology
Comparing Downstream Model Performance Metrics
Assessing Feature Importance Consistency
Hyperparameter Optimization Effects
Hands-on practical: Running TSTR Evaluations
Chapter 4: Privacy Assessment Techniques
Understanding Privacy Risks in Synthetic Data
Membership Inference Attacks (MIAs)
Attribute Inference Attacks
Distance-Based Privacy Metrics
Differential Privacy Considerations (if applicable)
Hands-on practical: Implementing a Basic MIA
Chapter 5: Specialized and Model-Specific Metrics
Evaluating Synthetic Images: FID, IS, Precision, Recall
Evaluating Synthetic Text: Perplexity, BLEU Scores
Evaluating Synthetic Time-Series Data
Metrics for GAN Evaluation
Metrics for VAE Evaluation
Hands-on practical: Calculating FID for Image Data
Chapter 6: Building Comprehensive Evaluation Reports
Selecting Appropriate Metrics for the Task
Automating Evaluation Pipelines
Visualizing Evaluation Results Effectively
Interpreting and Communicating Findings
Benchmarking Different Synthetic Datasets
Practice: Generating a Quality Report Snippet
Multivariate Distribution Comparisons
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- A Kernel Two-Sample Test, Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alex Smola, 2012 Journal of Machine Learning Research, Vol. 13 DOI: 10.5555/2810848.2810903 - Introduces the Maximum Mean Discrepancy (MMD) as a non-parametric kernel-based method for two-sample testing, which is central to quantitative multivariate comparison.
- Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, 2014 Advances in Neural Information Processing Systems, Vol. 27 (NeurIPS) DOI: 10.48550/arXiv.1406.2661 - Presents the original concept of Generative Adversarial Networks (GANs), foundational for discriminator-based evaluation of synthetic data quality.
- A Survey of Metrics for Generative Models, Zhifeng Wang, Jun Zou, 2020 ACM Computing Surveys, Vol. 53 (ACM) DOI: 10.1145/3403986 - A comprehensive review of metrics for evaluating generative models, covering various quantitative methods for assessing synthetic data quality, including fidelity and diversity aspects.
- Elements of Information Theory, Thomas M. Cover and Joy A. Thomas, 2006 (Wiley) - A standard textbook on information theory, essential for understanding metrics like Kullback-Leibler (KL) and Jensen-Shannon (JS) Divergence used in distribution comparisons.