Semantic Subspace

Semantic subspaces represent low-dimensional representations of high-dimensional data, aiming to capture the essential semantic information while discarding irrelevant details. Current research focuses on identifying and leveraging these subspaces within various models, including diffusion models, transformers, and GANs, often employing techniques like singular value decomposition, Fréchet means, and nuclear norm-based losses to achieve this. This work is significant because effectively identifying and utilizing semantic subspaces improves the controllability, robustness, and interpretability of machine learning models across diverse applications such as image editing, natural language processing, and visual reasoning.