  Deep generative models are powerful tools that have produced impressive
results in recent years. These advances have been for the most part empirically
driven, making it essential that we use high quality evaluation metrics. In
this paper, we provide new insights into the Inception Score, a recently
proposed and widely used evaluation metric for generative models, and
demonstrate that it fails to provide useful guidance when comparing models. We
discuss both suboptimalities of the metric itself and issues with its
application. Finally, we call for researchers to be more systematic and careful
when evaluating and comparing generative models, as the advancement of the
field depends upon it.
