Space Generative Model

Space generative models aim to create new data points that resemble a given dataset, but operating in high-dimensional or even infinite-dimensional spaces. Current research focuses on developing novel architectures, such as flow-based models and score-based generative models, often incorporating techniques like low-rank tensor decompositions to handle the complexity of high-dimensional data. These models find applications in diverse fields, including medical imaging (e.g., accelerating MRI reconstruction) where they improve image quality and reduce scan times, demonstrating significant practical impact. The development of robust and efficient methods for generating data in complex spaces remains a key area of ongoing investigation.