  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.
