Latent Space Communication

Latent space communication focuses on enabling seamless interaction and information exchange between independently trained neural networks by aligning their latent representations. Current research emphasizes developing methods to translate between these spaces, often leveraging relative representations or invariant features to overcome inconsistencies arising from differing training procedures and architectures. This work is significant for improving model reusability, facilitating zero-shot transfer learning across modalities, and optimizing resource allocation in applications like multi-agent communication systems. The resulting advancements promise to enhance efficiency and interoperability in various fields, including AI-mediated communication and wireless networks.