What is the degradation rate of new tabular generative evaluation metrics under adversarial perturbations comp

Abstract

Abstract: The growth of generative adversarial network (GAN) models has increased the ability of image processing and provides numerous industries with the technology to produce realistic image transformations. However, with the field being recently established there are new evaluation metrics that can further this research. Previous research has shown the Frchet Inception Distance (FID) to be an effective metric when testing these image-to-image GANs in real-world applications. Signed Inception Distance (SID), a founded metric in 2023, expands on FID by allowing unsigned distances. This paper uses pub

Research Question

What is the degradation rate of new tabular generative evaluation metrics under adversarial perturbations compared to standard statistical distance metrics in low-resource training regimes?

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