LGMLApr 6, 2019

Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models

arXiv:1904.03445v39 citations
AI Analysis

This addresses a fundamental limitation in generative models for researchers and practitioners, offering a method to handle complex latent structures, though it appears incremental as it builds on existing interpolation and geodesic concepts.

The paper tackles the problem of interpolating between points in latent spaces of generative models, particularly for non-Gaussian densities, by introducing a unified approach that also finds geodesics, with examples showing effectiveness in arbitrary densities and feature-based subsets.

Interpolating between points is a problem connected simultaneously with finding geodesics and study of generative models. In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space. However, this interpolation uses implicitly the fact that Gaussian is unimodal. Thus the problem of interpolating in the case when the latent density is non-Gaussian is an open problem. In this paper, we present a general and unified approach to interpolation, which simultaneously allows us to search for geodesics and interpolating curves in latent space in the case of arbitrary density. Our results have a strong theoretical background based on the introduced quality measure of an interpolating curve. In particular, we show that maximising the quality measure of the curve can be equivalently understood as a search of geodesic for a certain redefinition of the Riemannian metric on the space. We provide examples in three important cases. First, we show that our approach can be easily applied to finding geodesics on manifolds. Next, we focus our attention in finding interpolations in pre-trained generative models. We show that our model effectively works in the case of arbitrary density. Moreover, we can interpolate in the subset of the space consisting of data possessing a given feature. The last case is focused on finding interpolation in the space of chemical compounds.

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