Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for
  Generative Models   We introduce a new local sparse attention layer that preserves
two-dimensional geometry and locality. We show that by just replacing the dense
attention layer of SAGAN with our construction, we obtain very significant FID,
Inception score and pure visual improvements. FID score is improved from
$18.65$ to $15.94$ on ImageNet, keeping all other parameters the same. The
sparse attention patterns that we propose for our new layer are designed using
a novel information theoretic criterion that uses information flow graphs. We
also present a novel way to invert Generative Adversarial Networks with
attention. Our method extracts from the attention layer of the discriminator a
saliency map, which we use to construct a new loss function for the inversion.
This allows us to visualize the newly introduced attention heads and show that
they indeed capture interesting aspects of two-dimensional geometry of real
images.

25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph