Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Duality Gap as a Measure for Monitoring GAN Training   Generative adversarial network (GAN) is among the most popular deep learning
models for learning complex data distributions. However, training a GAN is
known to be a challenging task. This is often attributed to the lack of
correlation between the training progress and the trajectory of the generator
and discriminator losses and the need for the GAN's subjective evaluation. A
recently proposed measure inspired by game theory - the duality gap, aims to
bridge this gap. However, as we demonstrate, the duality gap's capability
remains constrained due to limitations posed by its estimation process. This
paper presents a theoretical understanding of this limitation and proposes a
more dependable estimation process for the duality gap. At the crux of our
approach is the idea that local perturbations can help agents in a zero-sum
game escape non-Nash saddle points efficiently. Through exhaustive
experimentation across GAN models and datasets, we establish the efficacy of
our approach in capturing the GAN training progress with minimal increase to
the computational complexity. Further, we show that our estimate, with its
ability to identify model convergence/divergence, is a potential performance
measure that can be used to tune the hyperparameters of a GAN.

37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math