  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.
