  The field of deep generative modeling has succeeded in producing
astonishingly realistic-seeming images and audio, but quantitative evaluation
remains a challenge. Log-likelihood is an appealing metric due to its grounding
in statistics and information theory, but it can be challenging to estimate for
implicit generative models, and scalar-valued metrics give an incomplete
picture of a model's quality. In this work, we propose to use rate distortion
(RD) curves to evaluate and compare deep generative models. While estimating RD
curves is seemingly even more computationally demanding than log-likelihood
estimation, we show that we can approximate the entire RD curve using nearly
the same computations as were previously used to achieve a single
log-likelihood estimate. We evaluate lossy compression rates of VAEs, GANs, and
adversarial autoencoders (AAEs) on the MNIST and CIFAR10 datasets. Measuring
the entire RD curve gives a more complete picture than scalar-valued metrics,
and we arrive at a number of insights not obtainable from log-likelihoods
alone.
