Flow Models allow for exact log-likelihood computation and exact inference, so if training Flow Models and GANs had the same computational cost, GANs might not be useful. AIS - propose putting a Gaussian observation model on the outputs of a GAN and using annealed importance sampling to estimate the log likelihood under this model, but show that estimates computed this way are inaccurate in the case where the GAN generator is also a flow model The generator being a flow model allows for computation of exact log-likelihoods in this case.