Particle Filter Bridge Interpolation
Adam Lindhe, Carl Ringqvist, Henrik Hult

TL;DR
This paper introduces an enhanced particle filter bridge interpolation method for autoencoding models, utilizing a discriminator network and sequential Monte Carlo to generate diverse, semantically meaningful interpolations in latent space.
Contribution
It extends previous bridge process methods by integrating a discriminator network and sequential Monte Carlo, improving variability and density alignment of interpolation paths.
Findings
Enhanced interpolation variability with the discriminator network.
Paths tend to reside in high-density, semantically meaningful regions.
Improved sampling efficiency and diversity in latent space interpolations.
Abstract
Auto encoding models have been extensively studied in recent years. They provide an efficient framework for sample generation, as well as for analysing feature learning. Furthermore, they are efficient in performing interpolations between data-points in semantically meaningful ways. In this paper, we build further on a previously introduced method for generating canonical, dimension independent, stochastic interpolations. Here, the distribution of interpolation paths is represented as the distribution of a bridge process constructed from an artificial random data generating process in the latent space, having the prior distribution as its invariant distribution. As a result the stochastic interpolation paths tend to reside in regions of the latent space where the prior has high mass. This is a desirable feature since, generally, such areas produce semantically meaningful samples. In…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Gaussian Processes and Bayesian Inference · Anomaly Detection Techniques and Applications
