Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Fuse Music Genres with Generative Adversarial Dual Learning   FusionGAN is a novel genre fusion framework for music generation that
integrates the strengths of generative adversarial networks and dual learning.
In particular, the proposed method offers a dual learning extension that can
effectively integrate the styles of the given domains. To efficiently quantify
the difference among diverse domains and avoid the vanishing gradient issue,
FusionGAN provides a Wasserstein based metric to approximate the distance
between the target domain and the existing domains. Adopting the Wasserstein
distance, a new domain is created by combining the patterns of the existing
domains using adversarial learning. Experimental results on public music
datasets demonstrated that our approach could effectively merge two genres.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph