Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Super-Resolution With Deep Variational Autoencoders   Image super-resolution (SR) techniques are used to generate a high-resolution
image from a low-resolution image. Until now, deep generative models such as
autoregressive models and Generative Adversarial Networks (GANs) have proven to
be effective at modelling high-resolution images. Models based on Variational
Autoencoders (VAEs) have often been criticized for their feeble generative
performance, but with new advancements such as VDVAE (very deep VAE), there is
now strong evidence that deep VAEs have the potential to outperform current
state-of-the-art models for high-resolution image generation. In this paper, we
introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE
methodologies to improve upon image super-resolution using transfer learning on
pretrained VDVAEs. Through qualitative and quantitative evaluations, we show
that the proposed model is competitive with other state-of-the-art methods.

37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph