Using Skew to Assess the Quality of GAN-generated Image Features

arXiv:2310.20636v2 Announce Type: replace-cross
Abstract: The rapid advancement of Generative Adversarial Networks (GANs) necessitates the need to robustly evaluate these models. Among the established evaluation criteria, the Fr\'{e}chetInception Distance (FID) has been widely adopted due to its conceptual simplicity, fast computation time, and strong correlation with human perception. However, FID has inherent limitations, mainly stemming from its assumption that feature embeddings follow a Gaussian distribution, and therefore can be defined by their first two moments. As this does not hold …

abstract advancement adversarial arxiv computation correlation cs.cv cs.lg evaluation features gan gans generated generative generative adversarial networks however human image networks perception quality simplicity skew type