Invertible Generative Model

Invertible generative models aim to create probabilistic models capable of both generating new data samples and reconstructing the latent variables that produced a given observation. Current research focuses on extending these models to handle non-invertible processes, such as those involving information loss, and improving their efficiency and scalability for high-dimensional data, often employing flow-based models and diffusion processes. These advancements are crucial for applications like image reconstruction, domain translation, and continual learning, where the ability to accurately represent and manipulate complex data distributions is paramount.