In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variables 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.
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