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 number of seeds. Our approach can be used to find semantic correspondence between different domains, align their relative spaces, and achieve competitive results in several tasks.

翻译：利用相对代表方式进行潜潜伏嵌入表明有可能使潜伏空间通信和零光模型在广泛的应用中进行缝合,然而,相对代表方式依赖一定数量的平行锚作为投入,在某些情形中可能不切实际,难以获得。为了克服这一局限性,我们建议采用一种优化方法,从有限的种子中发现新的平行锚。我们的方法可以用来在不同领域之间找到语义对应,调整其相对空间,并在若干任务中取得竞争性结果。</s>