  In this paper, we present a general method that can improve the sample
quality of pre-trained likelihood based generative models. Our method
constructs an energy function on the latent variable space that yields an
energy function on samples produced by the pre-trained generative model. The
energy based model is efficiently trained by maximizing the data likelihood,
and after training, new samples in the latent space are generated from the
energy based model and passed through the generator to producing samples in
observation space. We show that using our proposed method, we can greatly
improve the sample quality of popular likelihood based generative models, such
as normalizing flows and VAEs, with very little computational overhead.
