Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Better Global Loss Landscape of GANs   Understanding of GAN training is still very limited. One major challenge is
its non-convex-non-concave min-max objective, which may lead to sub-optimal
local minima. In this work, we perform a global landscape analysis of the
empirical loss of GANs. We prove that a class of separable-GAN, including the
original JS-GAN, has exponentially many bad basins which are perceived as
mode-collapse. We also study the relativistic pairing GAN (RpGAN) loss which
couples the generated samples and the true samples. We prove that RpGAN has no
bad basins. Experiments on synthetic data show that the predicted bad basin can
indeed appear in training. We also perform experiments to support our theory
that RpGAN has a better landscape than separable-GAN. For instance, we
empirically show that RpGAN performs better than separable-GAN with relatively
narrow neural nets. The code is available at https://github.com/AilsaF/RS-GAN.

3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin