Reviewing FID and SID Metrics on Generative Adversarial Networks

• URL: http://arxiv.org/abs/2402.03654v1
• Date: Tue, 6 Feb 2024 03:02:39 GMT
• Title: Reviewing FID and SID Metrics on Generative Adversarial Networks
• Authors: Ricardo de Deijn, Aishwarya Batra, Brandon Koch, Naseef Mansoor, Hema Makkena
• Abstract summary: The growth of generative adversarial network (GAN) models has increased the ability of image processing. 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. This paper uses public datasets that consist of faccades, cityscapes, and maps within Pix2Pix and CycleGAN models. After training these models are evaluated on both distance metrics which measure the generating performance of the trained models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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

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