Evaluation
Measure the property the application needs. Explore task-specific baselines, held-out data, slice analysis, preservation tests, and operational checks across synthetic data and media workflows. These guides keep different dimensions of quality visible rather than hiding them in a single unexplained score.
Choose the acceptance question before reviewing the outputs. Keep the comparison protocol fixed, document limitations, and inspect cases that are easy to miss in an average. A strong evaluation can support deployment, a narrower scope, or a decision to use a simpler approach; all are useful engineering outcomes.
Image Translation and Semantic Layouts: Preserve What Matters
Compare image-to-image and layout-conditioned workflows, with explicit class maps, alignment rules, and preservation tests.
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Super Resolution: Better-Looking Is Not Always More Faithful
Evaluate upscaling with honest baselines, realistic inputs, artifact review, and a clear distinction between inferred and observed detail.
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GANs for Fraud Detection: An Experiment Without Data Leakage
Design a defensive augmentation experiment with time-aware splits, fair baselines, and thresholds tied to a real review workflow.
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Evaluating CTGAN: More Than a Realistic-Looking Table
Check validity, relationships, downstream utility, and disclosure risk before accepting a synthetic dataset.
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Synthetic Healthcare Data: Validate Utility and Privacy Separately
A purpose-limited workflow for synthetic patient records, covering schema design, utility testing, privacy review, and release decisions.
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Style Transfer APIs: Change the Look, Preserve the Brief
Build a domain-based stylization workflow with supported controls, explicit preservation rules, and useful creative review.
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Video Prediction GANs: Evaluate Possible Futures, Not Certainty
Define context, prediction horizons, temporal baselines, and sampling rules for a more inspectable video prediction workflow.
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