  The use of relative representations for latent embeddings has shown potential
in enabling latent space communication and zero-shot model stitching across a
wide range of applications. Nevertheless, relative representations rely on a
certain amount of parallel anchors to be given as input, which can be
impractical to obtain in certain scenarios. To overcome this limitation, we
propose an optimization-based method to discover new parallel anchors from a
limited known set (seed). Our approach can be used to find semantic
correspondence between different domains, align their relative spaces, and
achieve competitive results in several tasks.
