What FID Hides: Detecting, Ranking, and Diagnosing Deviations in Generative Evaluation
Generative models are commonly ranked by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), yet FID's first-two-moment summary can miss distributional differences, and a reported scalar gap alone is not a calibrated test against sampling variation. FID's moment restriction has concrete consequences: on ImageNet, visually unrecognizable images optimized only to match the reference Inception mean and covariance obtain FID $24.7$ versus $58.6$ for held-out real images (lower is better). Moreover, FID and KID are scalar discrepancies that are unchanged when the two samples are exchanged and therefore do not encode the direction of a dispersion change: under-dispersion, as c...
| Authors | Hao Chen |
|---|---|
| Primary category | stat.ML |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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