Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unpriortized Autoencoder For Image Generation   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.

35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg