This work is an update of a previous paper on the same topic published a few years ago. 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, Fr\'echet 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. For example, in addition to quality and diversity of synthesized images, generative models should be evaluated in terms of bias and fairness. I describe new dimensions that are becoming important in assessing models , and discuss the connection between GAN evaluation and deepfakes  .
This work is an update of a previous paper on the same topic published a few years ago. 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, Frechet 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 .
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Fr\'echet
Frechet
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For example, in addition to quality and diversity of synthesized images, generative models should be evaluated in terms of bias and fairness.
Here,
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(e.g. bias and fairness) and
A
. These are important areas of concern in the machine learning community today and progress in GAN evaluation can help mitigate them