The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the development of various methods to obtain communication between latent spaces. "Latent space communication" can be achieved in two ways: i) by independently mapping the original spaces to a shared or relative one; ii) by directly estimating a transformation from a source latent space to a target one. In this work, we combine the two into a novel method to obtain latent space translation through the relative space. By formalizing the invertibility of angle-preserving relative representations and assuming the scale invariance of decoder modules in neural models, we can effectively use the relative space as an intermediary, independently projecting onto and from other semantically similar spaces. Extensive experiments over various architectures and datasets validate our scale invariance assumption and demonstrate the high accuracy of our method in latent space translation. We also apply our method to zero-shot stitching between arbitrary pre-trained text and image encoders and their classifiers, even across modalities. Our method has significant potential for facilitating the reuse of models in a practical manner via compositionality.

翻译：独立训练的神经模型之间相似表征的出现引起了表征学习社区的极大兴趣，促进了多种实现潜在空间间通信的方法发展。"潜在空间通信"可通过两种方式实现：i) 将原始空间独立映射到共享空间或相对空间；ii) 直接估计从源潜在空间到目标潜在空间的变换。本研究将这两种方法结合，提出一种通过相对空间实现潜在空间转换的新方法。通过形式化保角相对表征的可逆性，并假设神经模型中解码器模块的尺度不变性，我们可以有效地将相对空间作为中介，独立地投影到其他语义相似空间或从这些空间投影。在不同架构和数据集上的大量实验验证了我们的尺度不变性假设，并证明该方法在潜在空间转换中具有高精度。我们还将该方法应用于任意预训练文本与图像编码器及其分类器之间的零样本拼接，甚至能跨模态实现。该方法通过组合性为实际场景中的模型复用提供了重要潜力。