Pros and cons of GAN evaluation measures: New developments

This work is an update of my previous paper on the same topic published a few years ago (Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative and qualitative techniques to evaluate models has emerged. Although some measures such as Inception Score, Fréchet Inc...

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Veröffentlicht in:Computer vision and image understanding Jg. 215; S. 103329
1. Verfasser: Borji, Ali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.01.2022
Schlagworte:
Deepfakes
GAN evaluation
Generative modeling
Generative modeling
Deepfakes
GAN evaluation
41A10
65D05
65D17
41A05
ISSN:1077-3142, 1090-235X
Online-Zugang:Volltext
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Zusammenfassung:This work is an update of my previous paper on the same topic published a few years ago (Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative and qualitative techniques to evaluate models has emerged. Although some measures such as Inception Score, Fréchet Inception Distance, Precision–Recall, and Perceptual Path Length are relatively more popular, GAN evaluation is not a settled issue and there is still room for improvement. Here, I describe new dimensions that are becoming important in assessing models (e.g. bias and fairness) and discuss the connection between GAN evaluation and deepfakes. These are important areas of concern in the machine learning community today and progress in GAN evaluation can help mitigate them. •A critical review of new techniques for evaluating generative models.•A discussion of bias and fairness in the context of GANs and ways to mitigate them.•A discussion of how realistic deepfakes are and approaches to detect them.
ISSN:1077-3142
1090-235X
DOI:10.1016/j.cviu.2021.103329