Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Adversarial Networks for Image and Video Synthesis:
  Algorithms and Applications   The generative adversarial network (GAN) framework has emerged as a powerful
tool for various image and video synthesis tasks, allowing the synthesis of
visual content in an unconditional or input-conditional manner. It has enabled
the generation of high-resolution photorealistic images and videos, a task that
was challenging or impossible with prior methods. It has also led to the
creation of many new applications in content creation. In this paper, we
provide an overview of GANs with a special focus on algorithms and applications
for visual synthesis. We cover several important techniques to stabilize GAN
training, which has a reputation for being notoriously difficult. We also
discuss its applications to image translation, image processing, video
synthesis, and neural rendering.

3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
19
The categories of this paper are: eess