Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Image Decomposition in Vector Layers   Deep image generation is becoming a tool to enhance artists and designers
creativity potential. In this paper, we aim at making the generation process
more structured and easier to interact with. Inspired by vector graphics
systems, we propose a new deep image reconstruction paradigm where the outputs
are composed from simple layers, defined by their color and a vector
transparency mask. This presents a number of advantages compared to the
commonly used convolutional network architectures. In particular, our layered
decomposition allows simple user interaction, for example to update a given
mask, or change the color of a selected layer. From a compact code, our
architecture also generates vector images with a virtually infinite resolution,
the color at each point in an image being a parametric function of its
coordinates. We validate the efficiency of our approach by comparing
reconstructions with state-of-the-art baselines given similar memory resources
on CelebA and ImageNet datasets. Most importantly, we demonstrate several
applications of our new image representation obtained in an unsupervised
manner, including editing, vectorization and image search.

24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an