Title: Parametric Adversarial Divergences are Good Task Losses for Generative Modeling,
Abstract: Generative modeling of high dimensional data like images is a notoriously
difficult and ill-defined problem. In particular, how to evaluate a learned
generative model is unclear. In this position paper, we argue that adversarial
learning, pioneered with generative adversarial networks (GANs), provides an
interesting framework to implicitly define more meaningful task losses for
generative modeling tasks, such as for generating "visually realistic" images.
We refer to those task losses as parametric adversarial divergences and we give
two main reasons why we think parametric divergences are good learning
objectives for generative modeling. Additionally, we unify the processes of
choosing a good structured loss (in structured prediction) and choosing a
discriminator architecture (in generative modeling) using statistical decision
theory; we are then able to formalize and quantify the intuition that "weaker"
losses are easier to learn from, in a specific setting. Finally, we propose two
new challenging tasks to evaluate parametric and nonparametric divergences: a
qualitative task of generating very high-resolution digits, and a quantitative
task of learning data that satisfies high-level algebraic constraints. We use
two common divergences to train a generator and show that the parametric
divergence outperforms the nonparametric divergence on both the qualitative and
the quantitative task.