  In this paper, we treat the image generation task using an autoencoder, a
representative latent model. Unlike many studies regularizing the latent
variable's distribution by assuming a manually specified prior, we approach the
image generation task using an autoencoder by directly estimating the latent
distribution. To this end, we introduce 'latent density estimator' which
captures latent distribution explicitly and propose its structure. Through
experiments, we show that our generative model generates images with the
improved visual quality compared to previous autoencoder-based generative
models.
