Reviewing FID and SID Metrics on Generative Adversarial Networks
Ricardo de Deijn Aishwarya Batra Brandon Koch Naseef Mansoor Hema Makkena
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 Fr\'echet 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 public
datasets that consist of fa\c{c}ades, cityscapes, and maps within Pix2Pix and
CycleGAN models. After training these models are evaluated on both inception
distance metrics which measure the generating performance of the trained
models. Our findings indicate that usage of the metric SID incorporates an
efficient and effective metric to complement, or even exceed the ability shown
using the FID for the image-to-image GANs