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Any general artificial intelligence system must be able to interpret, operateon, and produce data in a multi-modal latent space that can represent audio,imagery, text, and more. In the last decade, deep neural networks have seenremarkable success in unimodal data distributions, while transfer learningtechniques have seen a massive expansion of model reuse across related domains.However, training multi-modal networks from scratch remains expensive andillusive, while heterogeneous transfer learning (HTL) techniques remainrelatively underdeveloped. In this paper, we propose a novel and cost-effectiveHTL strategy for co-embedding multi-modal spaces. Our method avoids costinefficiencies by preprocessing embeddings using pretrained models for allcomponents, without passing gradients through these models. We prove the use ofthis system in a joint image-audio embedding task. Our method has wide-reachingapplications, as successfully bridging the gap between different latent spacescould provide a framework for the promised "universal" embedding.

Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces