Generative adversarial networks (GANs) provide an algorithmic framework for
constructing generative models with several appealing properties: they do not
require a likelihood function to be specified, only a generating procedure;
they provide samples that are sharp and compelling; and they allow us to
harness our knowledge of building highly accurate neural network classifiers.
Here, we develop our understanding of GANs with the aim of forming a rich view
of this growing area of machine learning---to build connections to the diverse
set of statistical thinking on this topic, of which much can be gained by a
mutual exchange of ideas. We frame GANs within the wider landscape of
algorithms for learning in implicit generative models--models that only specify
a stochastic procedure with which to generate data--and relate these ideas to
modelling problems in related fields, such as econometrics and approximate
Bayesian computation. We develop likelihood-free inference methods and
highlight hypothesis testing as a principle for learning in implicit generative
models, using which we are able to derive the objective function used by GANs,
and many other related objectives. The testing viewpoint directs our focus to
the general problem of density ratio estimation. There are four approaches for
density ratio estimation, one of which is a solution using classifiers to
distinguish real from generated data. Other approaches such as divergence
minimisation and moment matching have also been explored in the GAN literature,
and we synthesise these views to form an understanding in terms of the
relationships between them and the wider literature, highlighting avenues for
future exploration and cross-pollination.

本文介绍了生成对抗网络（GANs）的算法框架，将其与隐式生成模型的学习算法相联系，提出了基于密度比估计的方法并借此引导观察者更深层次地思考生成模型。

隐式生成模型中的学习

Learning in Implicit Generative Models

Our paper deals with inferring simulator-based statistical models given some
observed data. A simulator-based model is a parametrized mechanism which
specifies how data are generated. It is thus also referred to as generative
model. We assume that only a finite number of parameters are of interest and
allow the generative process to be very general; it may be a noisy nonlinear
dynamical system with an unrestricted number of hidden variables. This weak
assumption is useful for devising realistic models but it renders statistical
inference very difficult. The main challenge is the intractability of the
likelihood function. Several likelihood-free inference methods have been
proposed which share the basic idea of identifying the parameters by finding
values for which the discrepancy between simulated and observed data is small.
A major obstacle to using these methods is their computational cost. The cost
is largely due to the need to repeatedly simulate data sets and the lack of
knowledge about how the parameters affect the discrepancy. We propose a
strategy which combines probabilistic modeling of the discrepancy with
optimization to facilitate likelihood-free inference. The strategy is
implemented using Bayesian optimization and is shown to accelerate the
inference through a reduction in the number of required simulations by several
orders of magnitude.

研究推断基于模拟器的统计模型，提出了结合概率建模和优化的策略来加速无似然推断方法，实现了对几个数量级所需模拟次数的削减。