Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Deeper Understanding of Adversarial Losses under a
  Discriminative Adversarial Network Setting   Recent work has proposed various adversarial loss functions for training
either generative or discriminative models. Yet, it remains unclear what
certain types of functions are valid adversarial losses, and how these loss
functions perform against one another. In this paper, we aim to gain a deeper
understanding of adversarial losses by decoupling the effects of their
component functions and regularization terms. We first derive in theory some
necessary and sufficient conditions of the component functions such that the
adversarial loss is a divergence-like measure between the data and the model
distributions. In order to systematically compare different adversarial losses,
we then propose a new, simple comparative framework, dubbed DANTest, based on
discriminative adversarial networks (DANs). With this framework, we evaluate an
extensive set of adversarial losses by combining different component functions
and regularization approaches. Our theoretical and empirical results can
together serve as a reference for choosing or designing adversarial training
objectives in future research.

14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th