Reviewing Fid and Sid Metrics on Generative Adversarial Networks


Ricardo de Deijn, Aishwarya Batra, Brandon Koch, Naseef Mansoor and Hema Makkena, Minnesota State University, United States of America


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 newevaluation metrics that can further this research. Previousresearch has shown the Frechet Inception Distance (FID)to be an effective metric when testing these image-to-image GANsin real-world applications. Signed Inception Distance (SID),a founded metric in 2023, expands on FID by allowing unsigned distances. This paper uses public datasetsthat consist of facades, 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.


Signed Inception Distance, Frechet Inception Distance, Generative Adversarial Networks, Supervised Image-to-Image Translation

Full Text  Volume 14, Number 2