Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence

• URL: http://arxiv.org/abs/2006.08242v3
• Date: Mon, 2 Nov 2020 09:26:53 GMT
• Title: Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
• Authors: Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
• Abstract summary: We propose a novel, efficient objective function that utilizes the Jensen-Shannon divergence for multiple distributions. It simultaneously approximates the unimodal and joint multimodal posteriors directly via a dynamic prior. In extensive experiments, we demonstrate the advantage of the proposed mmJSD model compared to previous work in unsupervised, generative learning tasks.
• Score: 20.23920009396818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from different data types is a long-standing goal in machine learning research, as multiple information sources co-occur when describing natural phenomena. However, existing generative models that approximate a multimodal ELBO rely on difficult or inefficient training schemes to learn a joint distribution and the dependencies between modalities. In this work, we propose a novel, efficient objective function that utilizes the Jensen-Shannon divergence for multiple distributions. It simultaneously approximates the unimodal and joint multimodal posteriors directly via a dynamic prior. In addition, we theoretically prove that the new multimodal JS-divergence (mmJSD) objective optimizes an ELBO. In extensive experiments, we demonstrate the advantage of the proposed mmJSD model compared to previous work in unsupervised, generative learning tasks.

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