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
Metrics for GAN Evaluation
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- Generative Adversarial Networks, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, 2014 Advances in Neural Information Processing Systems DOI: 10.48550/arXiv.1406.2661 - The foundational paper introducing Generative Adversarial Networks and their adversarial training framework, essential for understanding the underlying dynamics of GAN evaluation.
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) DOI: 10.5555/3295222.3295293 - Introduces the Fréchet Inception Distance (FID), a widely used and important metric for evaluating the quality and diversity of images generated by GANs.
- Improved Precision and Recall Metric for Assessing Generative Models, Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila, 2019 Advances in Neural Information Processing Systems, Vol. 32 (Neural Information Processing Systems Foundation) DOI: 10.48550/arXiv.1904.06991 - Presents an improved method for using Precision and Recall to evaluate generative models, providing specific insights into fidelity and diversity, and diagnosing issues like mode collapse.
- Wasserstein GAN, Martin Arjovsky, Soumith Chintala, Léon Bottou, 2017 Proceedings of the 34th International Conference on Machine Learning (ICML), Vol. 70 DOI: 10.5555/3305890.3305953 - Introduces the Wasserstein GAN, which addresses stability issues and mode collapse in GAN training, providing crucial context for understanding and diagnosing GAN convergence and failure modes.