Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Re-designing cities with conditional adversarial networks   This paper introduces a conditional generative adversarial network to
redesign a street-level image of urban scenes by generating 1) an urban
intervention policy, 2) an attention map that localises where intervention is
needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536)
after implementing the intervention. We also introduce a new dataset that
comprises aligned street-level images of before and after urban interventions
from real-life scenarios that make this research possible. The introduced
method has been trained on different ranges of urban interventions applied to
realistic images. The trained model shows strong performance in re-modelling
cities, outperforming existing methods that apply image-to-image translation in
other domains that is computed in a single GPU. This research opens the door
for machine intelligence to play a role in re-thinking and re-designing the
different attributes of cities based on adversarial learning, going beyond the
mainstream of facial landmarks manipulation or image synthesis from semantic
segmentation.

35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con