Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FusedProp: Towards Efficient Training of Generative Adversarial Networks   Generative adversarial networks (GANs) are capable of generating strikingly
realistic samples but state-of-the-art GANs can be extremely computationally
expensive to train. In this paper, we propose the fused propagation (FusedProp)
algorithm which can be used to efficiently train the discriminator and the
generator of common GANs simultaneously using only one forward and one backward
propagation. We show that FusedProp achieves 1.49 times the training speed
compared to the conventional training of GANs, although further studies are
required to improve its stability. By reporting our preliminary results and
open-sourcing our implementation, we hope to accelerate future research on the
training of GANs.

19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
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
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an