Reviewing FID and SID Metrics on Generative Adversarial Networks
It addresses the need for better evaluation metrics in GAN research, which is incremental as it builds on existing metrics like FID.
This paper evaluated the Signed Inception Distance (SID) metric against the Fréchet Inception Distance (FID) for assessing image-to-image GANs on datasets like façades and cityscapes, finding that SID is efficient and effective, potentially exceeding FID in performance.
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 Fréchet 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 façades, 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