@inproceedings{
josias2023multimodal,
title={Multimodal base distributions for continuous-time normalising flows},
author={Shane Josias and Willie Brink},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=eOODNEuD7D}
}
We investigate the utility of a multimodal base distribution in continuous-time normalising flows. Multimodality is incorporated through a Gaussian mixture model (GMM) centred at the empirical means of a target distribution's modes. In- and out-of-distribution likelihoods are reported for flows trained with a unimodal and multimodal base distribution. Our results show that the GMM base distribution leads to performance that is comparable to a standard (unimodal) Gaussian distribution for in-distribution likelihoods, but provides the ability to sample from a specific mode in the target distribution, yields generated samples of improved quality, and gives more reliable out-of-distribution likelihoods for low-dimensional input spaces. We conclude that a GMM base distribution is an attractive alternative to the standard base, whose inclusion incurs little to no cost and whose parameterisation may assist with more reliable out-of-distribution likelihoods.
Multimodal base distributions for continuous-time normalising flows
Shane Josias Willie Brink