Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quality Map Fusion for Adversarial Learning   Generative adversarial models that capture salient low-level features which
convey visual information in correlation with the human visual system (HVS)
still suffer from perceptible image degradations. The inability to convey such
highly informative features can be attributed to mode collapse, convergence
failure and vanishing gradients. In this paper, we improve image quality
adversarially by introducing a novel quality map fusion technique that
harnesses image features similar to the HVS and the perceptual properties of a
deep convolutional neural network (DCNN). We extend the widely adopted l2
Wasserstein distance metric to other preferable quality norms derived from
Banach spaces that capture richer image properties like structure, luminance,
contrast and the naturalness of images. We also show that incorporating a
perceptual attention mechanism (PAM) that extracts global feature embeddings
from the network bottleneck with aggregated perceptual maps derived from
standard image quality metrics translate to a better image quality. We also
demonstrate impressive performance over other methods.

32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics