PeerJ Comput Sci 2020 18;6:e274. Epub 2020 May 18.
Laboratory of Methods for Big Data Analysis, National Research University Higher School of Economics, Moscow, Russia.
Adversarial Optimization provides a reliable, practical way to match two implicitly defined distributions, one of which is typically represented by a sample of real data, and the other is represented by a parameterized generator. Matching of the distributions is achieved by minimizing a divergence between these distribution, and estimation of the divergence involves a secondary optimization task, which, typically, requires training a model to discriminate between these distributions. The choice of the model has its trade-off: high-capacity models provide good estimations of the divergence, but, generally, require large sample sizes to be properly trained. Read More