Latent Space Alignment

Latent space alignment aims to bridge the gap between different data representations by mapping them into a shared, lower-dimensional space. Current research focuses on aligning latent spaces from diverse sources, including different robot morphologies, satellite imagery with varying temporal resolutions, and even distinct languages in communication systems, often employing generative models (like VAEs and GANs) or specialized encoder-decoder architectures. This technique improves cross-domain transfer learning, enhances the robustness of models to data variations, and facilitates tasks like semantic segmentation, change detection, and cross-embodiment skill transfer, ultimately leading to more efficient and adaptable AI systems.