  Generative Adversarial Network (GAN) can be viewed as an implicit estimator
of a data distribution, and this perspective motivates using the adversarial
concept in the true input parameter estimation of black-box generators. While
previous works on likelihood-free inference introduces an implicit proposal
distribution on the generator input, this paper analyzes theoretic limitations
of the proposal distribution approach. On top of that, we introduce a new
algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the
analyzed limitations, so ALFI is able to find the posterior distribution on the
input parameter for black-box generative models. We experimented ALFI with
diverse simulation models as well as pre-trained statistical models, and we
identified that ALFI achieves the best parameter estimation accuracy with a
limited simulation budget.
