AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning

arXiv:2401.15948v2 Announce Type: replace
Abstract: Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions. Among these methods, explicit generators, such as Normalising Flows (NFs), in combination with the Metropolis Hastings algorithm have been extensively applied to get unbiased samples from target distributions. We systematically study central problems in conditional NFs, such as high variance, mode collapse and data efficiency. We propose adversarial training for NFs to ameliorate these problems. Experiments are conducted with low-dimensional synthetic datasets and …

abstract adversarial adversarial learning algorithm arxiv combination cond-mat.stat-mech cs.lg deep generative models generative generative models generators markov markov-chain-monte-carlo metropolis monte-carlo nfs physics.comp-ph samples sampling study type unbiased