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：相对表示在潜在嵌入中的应用已显示出在广泛任务中实现潜在空间通信与零样本模型拼接的潜力。然而，相对表示依赖于一定数量的并行锚点作为输入，这在某些场景下难以实际获取。为克服这一局限，我们提出一种基于优化的方法，从少量种子锚点中发现新的并行锚点。该方法可用于寻找不同域之间的语义对应关系、对齐其相对空间，并在多项任务中取得具有竞争力的结果。